diff --git a/lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb b/lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb
new file mode 100644
index 00000000..f574cd45
--- /dev/null
+++ b/lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb
@@ -0,0 +1,490 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training RL to do Cartpole Balancing\n",
+    "\n",
+    "This notebooks is part of [AI for Beginners Curriculum](http://aka.ms/ai-beginners). It has been inspired by [official PyTorch tutorial](https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html) and [this Cartpole Pytorch implementation](https://github.com/yc930401/Actor-Critic-pytorch).\n",
+    "\n",
+    "In this example, we will use RL to train a model to balance a pole on a cart that can move left and right on horizontal scale. We will use [OpenAI Gym](https://www.gymlibrary.ml/) environment to simulate the pole.\n",
+    "\n",
+    "> **Note**: You can run this lesson's code locally (eg. from Visual Studio Code), in which case the simulation will open in a new window. When running the code online, you may need to make some tweaks to the code, as described [here](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7).\n",
+    "\n",
+    "We will start by making sure Gym is installed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "!{sys.executable} -m pip install gym"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's create the CartPole environment and see how to operate on it. An environment has the following properties:\n",
+    "\n",
+    "* **Action space** is the set of possible actions that we can perform at each step of the simulation\n",
+    "* **Observation space** is the space of observations that we can make"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gym\n",
+    "\n",
+    "env = gym.make(\"CartPole-v1\")\n",
+    "\n",
+    "print(f\"Action space: {env.action_space}\")\n",
+    "print(f\"Observation space: {env.observation_space}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see how the simulation works. The following loop runs the simulation, until `env.step` does not return the termination flag `done`. We will randomly chose actions using `env.action_space.sample()`, which means the experiment will probably fail very fast (CartPole environment terminates when the speed of CartPole, its position or angle are outside certain limits).\n",
+    "\n",
+    "> Simulation will open in the new window. You can run the code several times and see how it behaves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "total_reward = 0\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   total_reward += rew\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "print(f\"Total reward: {total_reward}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Youn can notice that observations contain 4 numbers. They are:\n",
+    "- Position of cart\n",
+    "- Velocity of cart\n",
+    "- Angle of pole\n",
+    "- Rotation rate of pole\n",
+    "\n",
+    "`rew` is the reward we receive at each step. You can see that in CartPole environment you are rewarded 1 point for each simulation step, and the goal is to maximize total reward, i.e. the time CartPole is able to balance without falling.\n",
+    "\n",
+    "During reinforcement learning, our goal is to train a **policy** $\\pi$, that for each state $s$ will tell us which action $a$ to take, so essentially $a = \\pi(s)$.\n",
+    "\n",
+    "If you want probabilistic solution, you can think of policy as returning a set of probabilities for each action, i.e. $\\pi(a|s)$ would mean a probability that we should take action $a$ at state $s$.\n",
+    "\n",
+    "## Policy Gradient Method\n",
+    "\n",
+    "In simplest RL algorithm, called **Policy Gradient**, we will train a neural network to predict the next action."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "\n",
+    "num_inputs = 4\n",
+    "num_actions = 2\n",
+    "\n",
+    "model = torch.nn.Sequential(\n",
+    "    torch.nn.Linear(num_inputs, 128, bias=False, dtype=torch.float32),\n",
+    "    torch.nn.ReLU(),\n",
+    "    torch.nn.Linear(128, num_actions, bias = False, dtype=torch.float32),\n",
+    "    torch.nn.Softmax(dim=1)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will train the network by running many experiments, and updating our network after each run. Let's define a function that will run the experiment and return the results (so-called **trace**) - all states, actions (and their recommended probabilities), and rewards:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_episode(max_steps_per_episode = 10000,render=False):    \n",
+    "    states, actions, probs, rewards = [],[],[],[]\n",
+    "    state = env.reset()\n",
+    "    for _ in range(max_steps_per_episode):\n",
+    "        if render:\n",
+    "            env.render()\n",
+    "        action_probs = model(torch.from_numpy(np.expand_dims(state,0)))[0]\n",
+    "        action = np.random.choice(num_actions, p=np.squeeze(action_probs.detach().numpy()))\n",
+    "        nstate, reward, done, info = env.step(action)\n",
+    "        if done:\n",
+    "            break\n",
+    "        states.append(state)\n",
+    "        actions.append(action)\n",
+    "        probs.append(action_probs.detach().numpy())\n",
+    "        rewards.append(reward)\n",
+    "        state = nstate\n",
+    "    return np.vstack(states), np.vstack(actions), np.vstack(probs), np.vstack(rewards)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can run one episode with untrained network and observe that total reward (AKA length of episode) is very low:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s, a, p, r = run_episode()\n",
+    "print(f\"Total reward: {np.sum(r)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the tricky aspects of policy gradient algorithm is to use **discounted rewards**. The idea is that we compute the vector of total rewards at each step of the game, and during this process we discount the early rewards using some coefficient $gamma$. We also normalize the resulting vector, because we will use it as weight to affect our training: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eps = 0.0001\n",
+    "\n",
+    "def discounted_rewards(rewards,gamma=0.99,normalize=True):\n",
+    "    ret = []\n",
+    "    s = 0\n",
+    "    for r in rewards[::-1]:\n",
+    "        s = r + gamma * s\n",
+    "        ret.insert(0, s)\n",
+    "    if normalize:\n",
+    "        ret = (ret-np.mean(ret))/(np.std(ret)+eps)\n",
+    "    return ret"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's do the actual training! We will run 300 episodes, and at each episode we will do the following:\n",
+    "\n",
+    "1. Run the experiment and collect the trace\n",
+    "1. Calculate the difference (`gradients`) between the actions taken, and by predicted probabilities. The less the difference is, the more we are sure that we have taken the right action.\n",
+    "1. Calculate discounted rewards and multiply gradients by discounted rewards - that will make sure that steps with higher rewards will make more effect on the final result than lower-rewarded ones\n",
+    "1. Expected target actions for our neural network would be partly taken from the predicted probabilities during the run, and partly from calculated gradients. We will use `alpha` parameter to determine to which extent gradients and rewards are taken into account - this is called *learning rate* of reinforcement algorithm.\n",
+    "1. Finally, we train our network on states and expected actions, and repeat the process "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
+    "\n",
+    "def train_on_batch(x, y):\n",
+    "    x = torch.from_numpy(x)\n",
+    "    y = torch.from_numpy(y)\n",
+    "    optimizer.zero_grad()\n",
+    "    predictions = model(x)\n",
+    "    loss = -torch.mean(torch.log(predictions) * y)\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = 1e-4\n",
+    "\n",
+    "history = []\n",
+    "for epoch in range(300):\n",
+    "    states, actions, probs, rewards = run_episode()\n",
+    "    one_hot_actions = np.eye(2)[actions.T][0]\n",
+    "    gradients = one_hot_actions-probs\n",
+    "    dr = discounted_rewards(rewards)\n",
+    "    gradients *= dr\n",
+    "    target = alpha*np.vstack([gradients])+probs\n",
+    "    train_on_batch(states,target)\n",
+    "    history.append(np.sum(rewards))\n",
+    "    if epoch%100==0:\n",
+    "        print(f\"{epoch} -> {np.sum(rewards)}\")\n",
+    "\n",
+    "plt.plot(history)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's run the episode with rendering to see the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = run_episode(render=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hopefully, you can see that pole can now balance pretty well!\n",
+    "\n",
+    "## Actor-Critic Model\n",
+    "\n",
+    "Actor-Critic model is the further development of policy gradients, in which we build a neural network to learn both the policy and estimated rewards. The network will have two outputs (or you can view it as two separate networks):\n",
+    "* **Actor** will recommend the action to take by giving us the state probability distribution, as in policy gradient model\n",
+    "* **Critic** would estimate what the reward would be from those actions. It returns total estimated rewards in the future at the given state.\n",
+    "\n",
+    "Let's define such a model: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import count\n",
+    "import torch.nn.functional as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "env = gym.make(\"CartPole-v1\")\n",
+    "\n",
+    "state_size = env.observation_space.shape[0]\n",
+    "action_size = env.action_space.n\n",
+    "lr = 0.0001\n",
+    "\n",
+    "class Actor(torch.nn.Module):\n",
+    "    def __init__(self, state_size, action_size):\n",
+    "        super(Actor, self).__init__()\n",
+    "        self.state_size = state_size\n",
+    "        self.action_size = action_size\n",
+    "        self.linear1 = torch.nn.Linear(self.state_size, 128)\n",
+    "        self.linear2 = torch.nn.Linear(128, 256)\n",
+    "        self.linear3 = torch.nn.Linear(256, self.action_size)\n",
+    "\n",
+    "    def forward(self, state):\n",
+    "        output = F.relu(self.linear1(state))\n",
+    "        output = F.relu(self.linear2(output))\n",
+    "        output = self.linear3(output)\n",
+    "        distribution = torch.distributions.Categorical(F.softmax(output, dim=-1))\n",
+    "        return distribution\n",
+    "\n",
+    "\n",
+    "class Critic(torch.nn.Module):\n",
+    "    def __init__(self, state_size, action_size):\n",
+    "        super(Critic, self).__init__()\n",
+    "        self.state_size = state_size\n",
+    "        self.action_size = action_size\n",
+    "        self.linear1 = torch.nn.Linear(self.state_size, 128)\n",
+    "        self.linear2 = torch.nn.Linear(128, 256)\n",
+    "        self.linear3 = torch.nn.Linear(256, 1)\n",
+    "\n",
+    "    def forward(self, state):\n",
+    "        output = F.relu(self.linear1(state))\n",
+    "        output = F.relu(self.linear2(output))\n",
+    "        value = self.linear3(output)\n",
+    "        return value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We would need to slightly modify our `discounted_rewards` and `run_episode` functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discounted_rewards(next_value, rewards, masks, gamma=0.99):\n",
+    "    R = next_value\n",
+    "    returns = []\n",
+    "    for step in reversed(range(len(rewards))):\n",
+    "        R = rewards[step] + gamma * R * masks[step]\n",
+    "        returns.insert(0, R)\n",
+    "    return returns\n",
+    "\n",
+    "def run_episode(actor, critic, n_iters):\n",
+    "    optimizerA = torch.optim.Adam(actor.parameters())\n",
+    "    optimizerC = torch.optim.Adam(critic.parameters())\n",
+    "    for iter in range(n_iters):\n",
+    "        state = env.reset()\n",
+    "        log_probs = []\n",
+    "        values = []\n",
+    "        rewards = []\n",
+    "        masks = []\n",
+    "        entropy = 0\n",
+    "        env.reset()\n",
+    "\n",
+    "        for i in count():\n",
+    "            env.render()\n",
+    "            state = torch.FloatTensor(state).to(device)\n",
+    "            dist, value = actor(state), critic(state)\n",
+    "\n",
+    "            action = dist.sample()\n",
+    "            next_state, reward, done, _ = env.step(action.cpu().numpy())\n",
+    "\n",
+    "            log_prob = dist.log_prob(action).unsqueeze(0)\n",
+    "            entropy += dist.entropy().mean()\n",
+    "\n",
+    "            log_probs.append(log_prob)\n",
+    "            values.append(value)\n",
+    "            rewards.append(torch.tensor([reward], dtype=torch.float, device=device))\n",
+    "            masks.append(torch.tensor([1-done], dtype=torch.float, device=device))\n",
+    "\n",
+    "            state = next_state\n",
+    "\n",
+    "            if done:\n",
+    "                print('Iteration: {}, Score: {}'.format(iter, i))\n",
+    "                break\n",
+    "\n",
+    "\n",
+    "        next_state = torch.FloatTensor(next_state).to(device)\n",
+    "        next_value = critic(next_state)\n",
+    "        returns = discounted_rewards(next_value, rewards, masks)\n",
+    "\n",
+    "        log_probs = torch.cat(log_probs)\n",
+    "        returns = torch.cat(returns).detach()\n",
+    "        values = torch.cat(values)\n",
+    "\n",
+    "        advantage = returns - values\n",
+    "\n",
+    "        actor_loss = -(log_probs * advantage.detach()).mean()\n",
+    "        critic_loss = advantage.pow(2).mean()\n",
+    "\n",
+    "        optimizerA.zero_grad()\n",
+    "        optimizerC.zero_grad()\n",
+    "        actor_loss.backward()\n",
+    "        critic_loss.backward()\n",
+    "        optimizerA.step()\n",
+    "        optimizerC.step()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will run the main training loop. We will use manual network training process by computing proper loss functions and updating network parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "actor = Actor(state_size, action_size).to(device)\n",
+    "critic = Critic(state_size, action_size).to(device)\n",
+    "run_episode(actor, critic, n_iters=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let's close the environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "env.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Takeaway\n",
+    "\n",
+    "We have seen two RL algorithms in this demo: simple policy gradient, and more sophisticated actor-critic. You can see that those algorithms operate with abstract notions of state, action and reward - thus they can be applied to very different environments.\n",
+    "\n",
+    "Reinforcement learning allows us to learn the best strategy to solve the problem just by looking at the final reward. The fact that we do not need labelled datasets allows us to repeat simulations many times to optimize our models. However, there are still many challenges in RL, which you may learn if you decide to focus more on this interesting area of AI.   "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb b/lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb
index 5afa5a35..6315a1ce 100644
--- a/lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb
+++ b/lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb
@@ -17,23 +17,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: gym in c:\\winapp\\miniconda3\\envs\\py38\\lib\\site-packages (0.23.1)\n",
-      "Collecting pygame\n",
-      "  Downloading pygame-2.1.2-cp38-cp38-win_amd64.whl (8.4 MB)\n",
-      "Requirement already satisfied: importlib-metadata>=4.10.0 in c:\\winapp\\miniconda3\\envs\\py38\\lib\\site-packages (from gym) (4.11.3)\n",
-      "Requirement already satisfied: cloudpickle>=1.2.0 in c:\\winapp\\miniconda3\\envs\\py38\\lib\\site-packages (from gym) (2.0.0)\n",
-      "Requirement already satisfied: gym-notices>=0.0.4 in c:\\winapp\\miniconda3\\envs\\py38\\lib\\site-packages (from gym) (0.0.6)\n",
-      "Requirement already satisfied: numpy>=1.18.0 in c:\\winapp\\miniconda3\\envs\\py38\\lib\\site-packages (from gym) (1.22.3)\n",
-      "Requirement already satisfied: zipp>=0.5 in c:\\winapp\\miniconda3\\envs\\py38\\lib\\site-packages (from importlib-metadata>=4.10.0->gym) (3.6.0)\n",
-      "Installing collected packages: pygame\n",
-      "Successfully installed pygame-2.1.2\n"
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: gym in /home/leo/.local/lib/python3.10/site-packages (0.25.0)\n",
+      "Requirement already satisfied: pygame in /home/leo/.local/lib/python3.10/site-packages (2.1.2)\n",
+      "Requirement already satisfied: gym-notices>=0.0.4 in /home/leo/.local/lib/python3.10/site-packages (from gym) (0.0.7)\n",
+      "Requirement already satisfied: cloudpickle>=1.2.0 in /home/leo/.local/lib/python3.10/site-packages (from gym) (2.1.0)\n",
+      "Requirement already satisfied: numpy>=1.18.0 in /usr/lib/python3/dist-packages (from gym) (1.21.5)\n"
      ]
     }
    ],
@@ -54,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -64,10 +60,22 @@
       "Action space: Discrete(2)\n",
       "Observation space: Box([-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38], [4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38], (4,), float32)\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/leo/.local/lib/python3.10/site-packages/gym/core.py:329: DeprecationWarning: \u001b[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
+      "  deprecation(\n",
+      "/home/leo/.local/lib/python3.10/site-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: \u001b[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
+      "  deprecation(\n"
+     ]
     }
    ],
    "source": [
     "import gym\n",
+    "import pygame\n",
+    "import tqdm\n",
     "\n",
     "env = gym.make(\"CartPole-v1\")\n",
     "\n",
@@ -86,33 +94,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/leo/.local/lib/python3.10/site-packages/gym/core.py:57: DeprecationWarning: \u001b[33mWARN: You are calling render method, but you didn't specified the argument render_mode at environment initialization. To maintain backward compatibility, the environment will render in human mode.\n",
+      "If you want to render in human mode, initialize the environment in this way: gym.make('EnvName', render_mode='human') and don't call the render method.\n",
+      "See here for more information: https://www.gymlibrary.ml/content/api/\u001b[0m\n",
+      "  deprecation(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-0.04825775 -0.21918646 -0.00796685  0.27188525] -> 1.0\n",
-      "[-0.05264147 -0.4141938  -0.00252914  0.5620448 ] -> 1.0\n",
-      "[-0.06092535 -0.21903647  0.00871175  0.26856613] -> 1.0\n",
-      "[-0.06530608 -0.41428167  0.01408308  0.56398404] -> 1.0\n",
-      "[-0.07359171 -0.60959834  0.02536276  0.8610703 ] -> 1.0\n",
-      "[-0.08578368 -0.4148308   0.04258416  0.57646877] -> 1.0\n",
-      "[-0.09408029 -0.610523    0.05411354  0.88225687] -> 1.0\n",
-      "[-0.10629076 -0.4161761   0.07175867  0.6070649 ] -> 1.0\n",
-      "[-0.11461428 -0.22212696  0.08389997  0.33781925] -> 1.0\n",
-      "[-0.11905681 -0.0282929   0.09065636  0.07272854] -> 1.0\n",
-      "[-0.11962268  0.16542032  0.09211093 -0.19003159] -> 1.0\n",
-      "[-0.11631427 -0.03089041  0.08831029  0.13022853] -> 1.0\n",
-      "[-0.11693208 -0.22715913  0.09091487  0.44941387] -> 1.0\n",
-      "[-0.12147526 -0.42344147  0.09990314  0.76931363] -> 1.0\n",
-      "[-0.12994409 -0.61978614  0.11528942  1.0916848 ] -> 1.0\n",
-      "[-0.14233981 -0.81622297  0.13712311  1.4182041 ] -> 1.0\n",
-      "[-0.15866427 -1.01275     0.1654872   1.7504154 ] -> 1.0\n",
-      "[-0.17891927 -1.2093195   0.2004955   2.0896728 ] -> 1.0\n",
-      "[-0.20310566 -1.4058217   0.24228896  2.4370732 ] -> 1.0\n",
-      "Total reward: 19.0\n"
+      "[ 0.00425272 -0.19994313  0.00917169  0.34113726] -> 1.0\n",
+      "[ 0.00025386 -0.00495286  0.01599443  0.05136059] -> 1.0\n",
+      "[ 1.5480528e-04  1.8993615e-01  1.7021643e-02 -2.3623335e-01] -> 1.0\n",
+      "[ 0.00395353  0.38481084  0.01229698 -0.5234989 ] -> 1.0\n",
+      "[ 0.01164974  0.18951797  0.001827   -0.22696657] -> 1.0\n",
+      "[ 0.0154401   0.38461378 -0.00271233 -0.51907265] -> 1.0\n",
+      "[ 0.02313238  0.5797738  -0.01309379 -0.812609  ] -> 1.0\n",
+      "[ 0.03472786  0.38483363 -0.02934597 -0.5240733 ] -> 1.0\n",
+      "[ 0.04242453  0.580356   -0.03982743 -0.8258571 ] -> 1.0\n",
+      "[ 0.05403165  0.38580072 -0.05634458 -0.54596174] -> 1.0\n",
+      "[ 0.06174766  0.19151384 -0.06726381 -0.27155042] -> 1.0\n",
+      "[ 0.06557794 -0.00258703 -0.07269482 -0.00081817] -> 1.0\n",
+      "[ 0.0655262  -0.19659522 -0.07271118  0.26807207] -> 1.0\n",
+      "[ 0.0615943  -0.00051497 -0.06734974 -0.04662942] -> 1.0\n",
+      "[ 0.061584    0.19550486 -0.06828233 -0.3597784 ] -> 1.0\n",
+      "[ 0.06549409  0.00141663 -0.0754779  -0.08938391] -> 1.0\n",
+      "[ 0.06552242 -0.19254686 -0.07726558  0.17856352] -> 1.0\n",
+      "[ 0.06167149  0.00359088 -0.0736943  -0.1374588 ] -> 1.0\n",
+      "[ 0.0617433   0.19968675 -0.07644348 -0.45245075] -> 1.0\n",
+      "[ 0.06573704  0.3958018  -0.0854925  -0.7682167 ] -> 1.0\n",
+      "[ 0.07365308  0.20195423 -0.10085683 -0.50361156] -> 1.0\n",
+      "[ 0.07769216  0.0083876  -0.11092906 -0.24433874] -> 1.0\n",
+      "[ 0.07785992 -0.18498953 -0.11581583  0.01139782] -> 1.0\n",
+      "[ 0.07416012  0.01158649 -0.11558788 -0.31546465] -> 1.0\n",
+      "[ 0.07439185  0.20814891 -0.12189718 -0.64224803] -> 1.0\n",
+      "[ 0.07855483  0.01491799 -0.13474214 -0.3903015 ] -> 1.0\n",
+      "[ 0.07885319 -0.17806001 -0.14254816 -0.14295265] -> 1.0\n",
+      "[ 0.07529199  0.01878517 -0.14540721 -0.47699296] -> 1.0\n",
+      "[ 0.07566769 -0.17401667 -0.15494707 -0.23344138] -> 1.0\n",
+      "[ 0.07218736  0.0229406  -0.1596159  -0.57071024] -> 1.0\n",
+      "[ 0.07264617  0.21989843 -0.1710301  -0.9091196 ] -> 1.0\n",
+      "[ 0.07704414  0.02745241 -0.1892125  -0.6747003 ] -> 1.0\n",
+      "[ 0.07759319 -0.16460665 -0.20270652 -0.4470505 ] -> 1.0\n",
+      "[ 0.07430106 -0.35637102 -0.21164753 -0.22448184] -> 1.0\n",
+      "Total reward: 34.0\n"
      ]
     }
    ],
@@ -126,7 +159,9 @@
     "   obs, rew, done, info = env.step(env.action_space.sample())\n",
     "   total_reward += rew\n",
     "   print(f\"{obs} -> {rew}\")\n",
-    "print(f\"Total reward: {total_reward}\")"
+    "print(f\"Total reward: {total_reward}\")\n",
+    "\n",
+    "env.close()"
    ]
   },
   {
@@ -152,9 +187,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/tensorflow/__init__.py:29: DeprecationWarning: The distutils package is deprecated and slated for removal in Python 3.12. Use setuptools or check PEP 632 for potential alternatives\n",
+      "  import distutils as _distutils\n",
+      "2022-07-24 16:50:47.597258: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2022-07-24 16:50:47.597280: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
+      "/usr/local/lib/python3.10/dist-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib and slated for removal in Python 3.12; see the module's documentation for alternative uses\n",
+      "  import imp\n",
+      "2022-07-24 16:50:49.838826: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-07-24 16:50:49.839078: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2022-07-24 16:50:49.839143: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory\n",
+      "2022-07-24 16:50:49.839194: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory\n",
+      "2022-07-24 16:50:49.839245: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory\n",
+      "2022-07-24 16:50:49.839295: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory\n",
+      "2022-07-24 16:50:49.839345: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory\n",
+      "2022-07-24 16:50:49.839392: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory\n",
+      "2022-07-24 16:50:49.839441: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory\n",
+      "2022-07-24 16:50:49.839449: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
+      "Skipping registering GPU devices...\n",
+      "2022-07-24 16:50:49.839649: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import tensorflow as tf\n",
@@ -181,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -213,14 +274,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total reward: 13.0\n"
+      "Total reward: 27.0\n"
      ]
     }
    ],
@@ -238,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,38 +331,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 -> 44.0\n",
-      "100 -> 105.0\n",
-      "200 -> 145.0\n",
-      "300 -> 70.0\n",
-      "400 -> 190.0\n",
-      "500 -> 298.0\n",
-      "600 -> 289.0\n",
-      "700 -> 499.0\n",
-      "800 -> 499.0\n",
-      "900 -> 499.0\n"
+      "0 -> 29.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-07-24 16:50:51.475024: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 -> 135.0\n",
+      "200 -> 484.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-07-24 16:51:35.910774: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n",
+      "2022-07-24 16:51:37.151017: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n",
+      "2022-07-24 16:51:39.284311: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n",
+      "2022-07-24 16:51:42.235074: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n",
+      "2022-07-24 16:51:44.691458: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n",
+      "2022-07-24 16:51:48.381946: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x25a320af370>]"
+       "[<matplotlib.lines.Line2D at 0x7f40201e7b20>]"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -340,9 +419,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "error",
+     "evalue": "display Surface quit",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_44248/1459719159.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_episode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/tmp/ipykernel_44248/3855001447.py\u001b[0m in \u001b[0;36mrun_episode\u001b[0;34m(max_steps_per_episode, render)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_steps_per_episode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m             \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0maction_probs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_actions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction_probs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m                 )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrender_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0;34m\"\"\"Renders the environment.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m                 )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrender_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/wrappers/order_enforcing.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0;34m\"set `disable_render_order_enforcing=True` on the OrderEnforcer wrapper.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             )\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m                 )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrender_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0;34m\"\"\"Renders the environment.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m                 )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrender_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/wrappers/env_checker.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0menv_render_passive_checker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m                 )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrender_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/envs/classic_control/cartpole.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, mode)\u001b[0m\n\u001b[1;32m    215\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_render\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"human\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/envs/classic_control/cartpole.py\u001b[0m in \u001b[0;36m_render\u001b[0;34m(self, mode)\u001b[0m\n\u001b[1;32m    296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msurf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpygame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msurf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 298\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscreen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msurf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    299\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"human\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m             \u001b[0mpygame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31merror\u001b[0m: display Surface quit"
+     ]
+    }
+   ],
    "source": [
     "_ = run_episode(render=True)"
    ]
@@ -364,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -389,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -422,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -504,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -540,11 +643,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "16af2a8bbb083ea23e5e41c7f5787656b2ce26968575d8763f2c4b17f9cd711f"
-  },
   "kernelspec": {
-   "display_name": "Python 3.8.12 ('py38')",
+   "display_name": "Python 3.10.4 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -558,9 +658,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.10.4"
   },
-  "orig_nbformat": 4
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2
diff --git a/lessons/6-Other/22-DeepRL/README.md b/lessons/6-Other/22-DeepRL/README.md
index f5c631d0..26e3f691 100644
--- a/lessons/6-Other/22-DeepRL/README.md
+++ b/lessons/6-Other/22-DeepRL/README.md
@@ -83,6 +83,7 @@ After running one of those algorithms, we can expect our CartPole to behave like
 Continue your learning in the following notebooks:
 
 * [RL in TensorFlow](CartPole-RL-TF.ipynb)
+* [RL in PyTorch](CartPole-RL-PyTorch.ipynb)
 
 ## Other RL Tasks
 
diff --git a/lessons/6-Other/22-DeepRL/notebook.ipynb b/lessons/6-Other/22-DeepRL/notebook.ipynb
index b6338c89..50c2c33f 100644
--- a/lessons/6-Other/22-DeepRL/notebook.ipynb
+++ b/lessons/6-Other/22-DeepRL/notebook.ipynb
@@ -1,39 +1,15 @@
 {
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  },
-  "orig_nbformat": 4,
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
-  },
-  "interpreter": {
-   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
  "cells": [
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## CartPole Skating\n",
     "\n",
     "> **Problem**: If Peter wants to escape from the wolf, he needs to be able to move faster than him. We will see how Peter can learn to skate, in particular, to keep balance, using Q-Learning.\n",
     "\n",
     "First, let's install the gym and import required libraries:"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -41,23 +17,34 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "Requirement already satisfied: gym in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.18.3)\n",
-      "Requirement already satisfied: Pillow<=8.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (7.0.0)\n",
-      "Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.4.1)\n",
-      "Requirement already satisfied: numpy>=1.10.4 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.19.2)\n",
-      "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.6.0)\n",
-      "Requirement already satisfied: pyglet<=1.5.15,>=1.4.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.5.15)\n",
-      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
-      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Collecting gym\n",
+      "  Downloading gym-0.25.0.tar.gz (720 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m720.4/720.4 KB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25h  Installing build dependencies ... \u001b[?25ldone\n",
+      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
+      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
+      "\u001b[?25hRequirement already satisfied: numpy>=1.18.0 in /usr/lib/python3/dist-packages (from gym) (1.21.5)\n",
+      "Collecting gym-notices>=0.0.4\n",
+      "  Downloading gym_notices-0.0.7-py3-none-any.whl (2.7 kB)\n",
+      "Collecting cloudpickle>=1.2.0\n",
+      "  Downloading cloudpickle-2.1.0-py3-none-any.whl (25 kB)\n",
+      "Building wheels for collected packages: gym\n",
+      "  Building wheel for gym (pyproject.toml) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for gym: filename=gym-0.25.0-py3-none-any.whl size=824430 sha256=3f4ed647f1d12814bb457f7d83a7ccd0f682d12a0259ca07b7fab0db5100fc6e\n",
+      "  Stored in directory: /home/leo/.cache/pip/wheels/c0/3c/33/32d86254a5bd554f5f07759ae1794646e490dd5fa81ebdcda3\n",
+      "Successfully built gym\n",
+      "Installing collected packages: gym-notices, cloudpickle, gym\n",
+      "Successfully installed cloudpickle-2.1.0 gym-0.25.0 gym-notices-0.0.7\n"
      ]
     }
    ],
    "source": [
     "import sys\n",
-    "!pip install gym \n",
+    "!pip install gym pygame\n",
     "\n",
     "import gym\n",
     "import matplotlib.pyplot as plt\n",
@@ -66,86 +53,118 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Create a cartpole environment"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
-   "source": [
-    "env = gym.make(\"CartPole-v1\")\n",
-    "print(env.action_space)\n",
-    "print(env.observation_space)\n",
-    "print(env.action_space.sample())"
-   ],
    "cell_type": "code",
-   "metadata": {},
    "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Discrete(2)\n",
+      "Box([-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38], [4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38], (4,), float32)\n",
+      "1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
      "text": [
-      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n0\n"
+      "/home/leo/.local/lib/python3.10/site-packages/gym/core.py:329: DeprecationWarning: \u001b[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
+      "  deprecation(\n",
+      "/home/leo/.local/lib/python3.10/site-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: \u001b[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
+      "  deprecation(\n"
      ]
     }
+   ],
+   "source": [
+    "env = gym.make(\"CartPole-v1\")\n",
+    "print(env.action_space)\n",
+    "print(env.observation_space)\n",
+    "print(env.action_space.sample())"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "To see how the environment works, let's run a short simulation for 100 steps."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
-   "source": [
-    "env.reset()\n",
-    "\n",
-    "for i in range(100):\n",
-    "   env.render()\n",
-    "   env.step(env.action_space.sample())\n",
-    "env.close()"
-   ],
    "cell_type": "code",
-   "metadata": {},
    "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stderr",
+     "output_type": "stream",
      "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+      "/home/leo/.local/lib/python3.10/site-packages/gym/core.py:57: DeprecationWarning: \u001b[33mWARN: You are calling render method, but you didn't specified the argument render_mode at environment initialization. To maintain backward compatibility, the environment will render in human mode.\n",
+      "If you want to render in human mode, initialize the environment in this way: gym.make('EnvName', render_mode='human') and don't call the render method.\n",
+      "See here for more information: https://www.gymlibrary.ml/content/api/\u001b[0m\n",
+      "  deprecation(\n"
+     ]
+    },
+    {
+     "ename": "DependencyNotInstalled",
+     "evalue": "pygame is not installed, run `pip install gym[classic_control]`",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/envs/classic_control/cartpole.py\u001b[0m in \u001b[0;36m_render\u001b[0;34m(self, mode)\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0;32mimport\u001b[0m \u001b[0mpygame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpygame\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgfxdraw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pygame'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mDependencyNotInstalled\u001b[0m                    Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_32716/4123126963.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m    \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m    \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m                 )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrender_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0;34m\"\"\"Renders the environment.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m                 )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrender_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/wrappers/order_enforcing.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0;34m\"set `disable_render_order_enforcing=True` on the OrderEnforcer wrapper.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             )\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m                 )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrender_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0;34m\"\"\"Renders the environment.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m                 )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrender_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/wrappers/env_checker.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecked_render\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecked_render\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0menv_render_passive_checker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py\u001b[0m in \u001b[0;36menv_render_passive_checker\u001b[0;34m(env, *args, **kwargs)\u001b[0m\n\u001b[1;32m    322\u001b[0m             )\n\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m     \u001b[0;31m# TODO: Check that the result is correct\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m                 )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrender_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrender\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/envs/classic_control/cartpole.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, mode)\u001b[0m\n\u001b[1;32m    215\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_render\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"human\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.10/site-packages/gym/envs/classic_control/cartpole.py\u001b[0m in \u001b[0;36m_render\u001b[0;34m(self, mode)\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpygame\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgfxdraw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m             raise DependencyNotInstalled(\n\u001b[0m\u001b[1;32m    226\u001b[0m                 \u001b[0;34m\"pygame is not installed, run `pip install gym[classic_control]`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m             )\n",
+      "\u001b[0;31mDependencyNotInstalled\u001b[0m: pygame is not installed, run `pip install gym[classic_control]`"
      ]
     }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "for i in range(100):\n",
+    "   env.render()\n",
+    "   env.step(env.action_space.sample())\n",
+    "env.close()"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "During simulation, we need to get observations in order to decide how to act. In fact, `step` function returns us back current observations, reward function, and the `done` flag that indicates whether it makes sense to continue the simulation or not:"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
-   "source": [
-    "env.reset()\n",
-    "\n",
-    "done = False\n",
-    "while not done:\n",
-    "   env.render()\n",
-    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
-    "   print(f\"{obs} -> {rew}\")\n",
-    "env.close()"
-   ],
    "cell_type": "code",
-   "metadata": {},
    "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "[ 0.03044442 -0.19543914 -0.04496216  0.28125618] -> 1.0\n",
       "[ 0.02653564 -0.38989186 -0.03933704  0.55942606] -> 1.0\n",
@@ -168,14 +187,24 @@
       "[ 0.12921301  0.59883361 -0.22594088 -1.22169133] -> 1.0\n"
      ]
     }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "env.close()"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We can get min and max value of those numbers:"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -183,10 +212,11 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n",
+      "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
      ]
     }
    ],
@@ -196,11 +226,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## State Discretization"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -213,11 +243,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Let's also explore other discretization method using bins:"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -225,10 +255,11 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+      "Sample bins for interval (-5,5) with 10 bins\n",
+      " [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
      ]
     }
    ],
@@ -247,11 +278,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Let's now run a short simulation and observe those discrete environment values."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -259,10 +290,21 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "(0, 0, -1, -3)\n(0, 0, -2, 0)\n(0, 0, -2, -3)\n(0, 1, -3, -6)\n(0, 2, -4, -9)\n(0, 3, -6, -12)\n(0, 2, -8, -9)\n(0, 3, -10, -13)\n(0, 4, -13, -16)\n(0, 4, -16, -19)\n(0, 4, -20, -17)\n(0, 4, -24, -20)\n"
+      "(0, 0, -1, -3)\n",
+      "(0, 0, -2, 0)\n",
+      "(0, 0, -2, -3)\n",
+      "(0, 1, -3, -6)\n",
+      "(0, 2, -4, -9)\n",
+      "(0, 3, -6, -12)\n",
+      "(0, 2, -8, -9)\n",
+      "(0, 3, -10, -13)\n",
+      "(0, 4, -13, -16)\n",
+      "(0, 4, -16, -19)\n",
+      "(0, 4, -20, -17)\n",
+      "(0, 4, -24, -20)\n"
      ]
     }
    ],
@@ -279,11 +321,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Q-Table Structure"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -299,11 +341,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Let's Start Q-Learning!"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -323,8 +365,8 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "0: 108.0, alpha=0.3, epsilon=0.9\n"
      ]
@@ -370,11 +412,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Plotting Training Progress"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -382,25 +424,27 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
       ]
      },
+     "execution_count": 20,
      "metadata": {},
-     "execution_count": 20
+     "output_type": "execute_result"
     },
     {
-     "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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121.611515 180.203814 \r\nL 121.614558 68.108914 \r\nL 121.635864 80.563902 \r\nL 121.745436 203.90847 \r\nL 121.644995 74.537295 \r\nL 121.751523 181.810909 \r\nL 121.763698 44.404257 \r\nL 121.842833 190.24816 \r\nL 121.861095 138.419335 \r\nL 121.903707 206.319113 \r\nL 121.918925 34.359911 \r\nL 121.955449 107.482749 \r\nL 121.958493 37.975876 \r\nL 121.991973 208.729756 \r\nL 122.061977 119.535965 \r\nL 122.098501 185.0251 \r\nL 122.138069 28.735078 \r\nL 122.171549 153.284967 \r\nL 122.275034 76.14439 \r\nL 122.177637 195.069446 \r\nL 122.281121 150.070776 \r\nL 122.372431 95.831308 \r\nL 122.293296 201.497827 \r\nL 122.393737 98.241951 \r\nL 122.463741 206.720887 \r\nL 122.427217 86.992284 \r\nL 122.503309 94.224213 \r\nL 122.515484 198.283636 \r\nL 122.57027 44.002483 \r\nL 122.615925 167.748825 \r\nL 122.701147 69.314235 \r\nL 122.722453 194.667672 \r\nL 122.731584 77.751486 \r\nL 122.768108 199.890732 \r\nL 122.780283 54.448603 \r\nL 122.847243 161.722217 \r\nL 122.880724 50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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205.917339 \r\nL 127.13577 107.080976 \r\nL 127.150988 168.954146 \r\nL 127.199687 74.135521 \r\nL 127.248386 141.231752 \r\nL 127.26056 193.864124 \r\nL 127.312303 88.599379 \r\nL 127.354914 184.623326 \r\nL 127.370132 44.806031 \r\nL 127.418831 195.872993 \r\nL 127.464486 155.69561 \r\nL 127.50101 210.336851 \r\nL 127.546665 41.59184 \r\nL 127.567971 136.410466 \r\nL 127.589276 62.082306 \r\nL 127.59232 204.310244 \r\nL 127.677543 115.518226 \r\nL 127.695805 77.349712 \r\nL 127.74146 185.0251 \r\nL 127.759722 17.083636 \r\nL 127.851032 68.510687 \r\nL 127.878425 199.087184 \r\nL 127.887556 17.083636 \r\nL 127.963647 145.24949 \r\nL 128.061045 41.59184 \r\nL 128.064088 212.747494 \r\nL 128.073219 42.395388 \r\nL 128.109743 184.623326 \r\nL 128.1067 17.083636 \r\nL 128.185835 143.240621 \r\nL 128.201053 89.001153 \r\nL 128.252796 195.47122 \r\nL 128.295407 111.098714 \r\nL 128.3228 189.042838 \r\nL 128.383673 41.190067 \r\nL 128.404979 152.481419 \r\nL 128.462809 32.351042 \r\nL 128.423241 194.265898 \r\nL 128.514551 142.035299 \r\nL 128.56325 208.729756 \r\nL 128.566293 51.234412 \r\nL 128.624123 150.47255 \r\nL 128.730651 46.011353 \r\nL 128.715433 208.327982 \r\nL 128.736739 103.465011 \r\nL 128.751957 172.971885 \r\nL 128.74587 80.162129 \r\nL 128.803699 140.428204 \r\nL 128.858485 33.958137 \r\nL 128.864573 204.712018 \r\nL 128.913271 151.276098 \r\nL 128.919359 192.658803 \r\nL 128.998494 63.287627 \r\nL 129.016756 110.295166 \r\nL 129.077629 60.876984 \r\nL 129.025887 186.230421 \r\nL 129.114153 117.928869 \r\nL 129.117197 166.945277 \r\nL 129.175027 50.832639 \r\nL 129.223725 161.320443 \r\nL 129.321123 63.287627 \r\nL 129.308948 199.890732 \r\nL 129.333297 84.983415 \r\nL 129.357647 198.283636 \r\nL 129.409389 53.243282 \r\nL 129.442869 145.24949 \r\nL 129.518961 20.297827 \r\nL 129.491568 197.078315 \r\nL 129.549398 161.722217 \r\nL 129.640708 202.301375 \r\nL 129.610271 33.556364 \r\nL 129.655926 179.80204 \r\nL 129.716799 19.092506 \r\nL 129.668101 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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185.315461 191.453481 \r\nL 185.382421 198.283636 \r\nL 185.428076 17.083636 \r\nL 185.528518 201.497827 \r\nL 185.540692 155.293836 \r\nL 185.558954 193.060576 \r\nL 185.647221 17.083636 \r\nL 185.732443 211.943947 \r\nL 185.759836 119.937738 \r\nL 185.814622 195.47122 \r\nL 185.848103 17.083636 \r\nL 185.869408 127.571441 \r\nL 185.930282 17.083636 \r\nL 185.884627 179.400266 \r\nL 185.969849 52.841508 \r\nL 185.972893 196.274767 \r\nL 185.97898 17.083636 \r\nL 186.079421 141.633525 \r\nL 186.182906 17.083636 \r\nL 186.170731 196.676541 \r\nL 186.192037 31.547494 \r\nL 186.246823 197.480089 \r\nL 186.204212 17.083636 \r\nL 186.304653 138.821109 \r\nL 186.362482 213.149268 \r\nL 186.417268 17.083636 \r\nL 186.469011 202.703149 \r\nL 186.52684 50.430865 \r\nL 186.545102 199.087184 \r\nL 186.566408 21.503149 \r\nL 186.657718 171.364789 \r\nL 186.752072 17.083636 \r\nL 186.666849 206.319113 \r\nL 186.76729 95.027761 \r\nL 186.855556 183.819778 \r\nL 186.797727 17.083636 \r\nL 186.873818 80.965676 \r\nL 186.980347 17.083636 \r\nL 186.965128 209.533304 \r\nL 186.98339 25.119113 \r\nL 187.089919 196.676541 \r\nL 187.050351 17.083636 \r\nL 187.092962 156.900931 \r\nL 187.129486 17.083636 \r\nL 187.19036 194.667672 \r\nL 187.202534 74.939069 \r\nL 187.254277 205.917339 \r\nL 187.211665 17.083636 \r\nL 187.31515 120.339512 \r\nL 187.327325 211.943947 \r\nL 187.333412 17.083636 \r\nL 187.424722 178.596718 \r\nL 187.500814 17.083636 \r\nL 187.433853 209.935078 \r\nL 187.540381 42.395388 \r\nL 187.613429 198.68541 \r\nL 187.607342 17.083636 \r\nL 187.656041 172.971885 \r\nL 187.662128 17.083636 \r\nL 187.710827 188.23929 \r\nL 187.768656 73.331973 \r\nL 187.7717 189.846386 \r\nL 187.853879 17.083636 \r\nL 187.878228 57.26102 \r\nL 187.957364 17.083636 \r\nL 187.936058 203.90847 \r\nL 187.978669 93.420665 \r\nL 188.039543 199.087184 \r\nL 188.009106 17.083636 \r\nL 188.091285 166.945277 \r\nL 188.133896 195.872993 \r\nL 188.118678 17.083636 \r\nL 188.152158 78.956807 \r\nL 188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 91.81357 \r\nL 189.47311 17.083636 \r\nL 189.409193 213.952816 \r\nL 189.479197 119.535965 \r\nL 189.518765 198.283636 \r\nL 189.530939 30.743947 \r\nL 189.549201 146.454812 \r\nL 189.652686 17.083636 \r\nL 189.576594 191.051707 \r\nL 189.658773 186.632195 \r\nL 189.683123 17.083636 \r\nL 189.774433 63.287627 \r\nL 189.874874 212.747494 \r\nL 189.810957 17.083636 \r\nL 189.884005 86.59051 \r\nL 189.899223 17.083636 \r\nL 189.908354 197.078315 \r\nL 189.944878 185.0251 \r\nL 189.947922 195.872993 \r\nL 189.969227 17.083636 \r\nL 190.030101 136.812239 \r\nL 190.121411 17.083636 \r\nL 190.094018 205.515565 \r\nL 190.139673 154.892062 \r\nL 190.252288 17.083636 \r\nL 190.227939 211.140399 \r\nL 190.258376 52.439734 \r\nL 190.279681 191.855255 \r\nL 190.27055 17.083636 \r\nL 190.374035 170.159468 \r\nL 190.377079 174.177206 \r\nL 190.380122 54.046829 \r\nL 190.413603 17.083636 \r\nL 190.422734 191.453481 \r\nL 190.486651 50.029091 \r\nL 190.532306 202.703149 \r\nL 190.501869 17.083636 \r\nL 190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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320.128594 80.965676 \r\nL 320.162075 22.306696 \r\nL 320.204686 185.0251 \r\nL 320.235123 116.321774 \r\nL 320.320345 194.667672 \r\nL 320.286865 17.083636 \r\nL 320.338607 38.779424 \r\nL 320.402524 17.083636 \r\nL 320.387306 184.221552 \r\nL 320.432961 155.69561 \r\nL 320.51514 50.430865 \r\nL 320.442092 205.515565 \r\nL 320.554708 56.055698 \r\nL 320.652105 196.274767 \r\nL 320.566882 17.083636 \r\nL 320.66428 80.162129 \r\nL 320.691673 17.083636 \r\nL 320.703847 188.23929 \r\nL 320.75559 124.759024 \r\nL 320.840812 202.301375 \r\nL 320.776895 17.083636 \r\nL 320.859074 93.018891 \r\nL 320.959515 17.083636 \r\nL 320.941253 188.23929 \r\nL 320.97169 53.243282 \r\nL 321.047782 191.453481 \r\nL 321.063 17.083636 \r\nL 321.081262 67.305366 \r\nL 321.09648 188.641064 \r\nL 321.178659 17.083636 \r\nL 321.184747 71.323104 \r\nL 321.2791 198.68541 \r\nL 321.291275 17.083636 \r\nL 321.330843 183.418004 \r\nL 321.403891 92.617118 \r\nL 321.483026 17.083636 \r\nL 321.501288 176.989623 \r\nL 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rIiL1FclZNwY8Aixzzt0V9tBcYLqfng48G1Z+gT/7Zjiwo3SIR0REGl9aBHVGAT8ElphZ6SD4DcAc4AkzmwF8BUzzj70ATAFWAXuBi6La4gho6EZEpFydQe+ce5uarxM2sZr6DrjsMNslIiJRom/GiogEXCCDXl+YEhEpF8igFxGRcgp6EZGAC2TQry7aHe8miIgkjEAG/fKvd8W7CSIiCSOQQS8iIuUU9CIiAaegFxEJOAW9iEjAKehFRAJOQS8iEnAKehGRgFPQi4gEnIJeRCTgFPQiIgGnoBcRCTgFvYhIwCnoRUQCTkEvIhJwCnoRkYBT0IuIBFxggr5w2954N0FEJCEFIujfXf0No+94Ld7NEBFJSIEI+uVf74x3E0REElYggt65eLdARCRxBSPo490AEZEEFoigFxGRmgUi6J3GbkREahSIoBcRkZo1+aDfd/AQtz6/LN7NEBFJWHUGvZn9zsw2m9nSsLKOZjbPzFb63x18uZnZvWa2ysw+MbPBsWw8wINvfBHrRYiINGmR9Oj/AEyuVDYbmO+cywPm+3mAU4E8/zMTuD86zazZ3oPFsV6EiEiTVmfQO+feBLZWKp4KPOqnHwXODCv/owt5D2hvZlnRamz1DYzpq4uINHkNHaPv6pzbCOB/d/Hl2cC6sHqFvqwKM5tpZgvNbGFRUVEDmwH/WLyhwc8VEUkG0T4Ya9WUVdvnds495JzLd87lZ2ZmNniBG3bsa/BzRUSSQUODflPpkIz/vdmXFwLdw+rlAOpyi4jEUUODfi4w3U9PB54NK7/An30zHNhROsQjIiLxkVZXBTP7CzAO6GxmhcDPgDnAE2Y2A/gKmOarvwBMAVYBe4GLYtBmERGphzqD3jl3bg0PTaymrgMuO9xGiYhI9DT5b8aKiEjtFPQiIgHXpIP+/TWVv8clIiKVNemg/8HDC+LdBBGRhNekg/7AoZJ4N0FEJOE16aAXEZG6KehFRAJOQS8iEnAKehGRgFPQi4gEnIJeRCTgFPQiInHUPC32MaygFxGJo7OH5MR8GQp6EZE4Om9Yj5gvQ0EvIhJHA7LbxXwZCnoRkTjp3aV1oyxHQS8iEidj8zIbZTkKehGROLnmlD6NshwFvYhInLRsXufdXKNCQS8iEnAKeklIfbo2zkEqaTzpjfDFIKme/vKSkJyLdwsk2sbkdY53E5KWgl6kEfzyO8dGXPfYRjivuql69OJhUX29Fs1So/p63Tu2iOrrRYuCXqSBxveNzalxd04bGPUAasq+Oyi7bPrEPuV/87F9av/7FxyXVedr5+d2qHd7Tu7ftcJ8r8xWjIvReyFaFPRS5vwTYv9V7Ibo27VNteV/nnFClbJOrZrHtC3nDO1eNn36wG4AXDDiSNbOKaj1ec1SrV7LuXh0br3bVpe1cwq45uTGOZ0vmobkdqBV86offJUDd3CP9hXmczrU3btOT0vh0nFHlQ0rRXKBsXF9u1SYP2twDtntQ8tqlpqYkZqYrZKo6pXZKqJ6t9VjeKHyP1ldLh/fO+K6o3tXHMt9+eqxVercd94gRsdhzLe095bZJp0zBnbjigm9+ckpfWt9zohenfhOWK80El3bZgBUG3CljvB1EtWpA4447Nd49rJRnDesBwtunMTim08GKgb43y4Zwc2n9WfBDRN57N+H07LS36tTq+ZMOrrm9+pPTunHdZNDPwBnDc7m/RsncvGonmV1hlbq9We2Secfl48um+/SJp3Zp/bjigm9Oe3YmvcirpjQm/wj678HEQ0K+iZkUKUeS6T+3/mDK7xxo2FafndunHI0AMNyO9ZZ/5pKYTiqd6da6885K/Sh819Tj6n28dOO6xZJMyOWF/ZV9PvOG8S8aj5cSv3homF8cOMk0lJTmHVyX9pkNAPgwpG5NK+mR/eXmcNJS03hhin9uHBkbll5Zpt0js5qW+0y+vi9mP+aOqDC3kL3ji147ZpxADzxHyPKyksD5KcFR5eVfS+/6lURv5ffvcK6Ho7OrdOrLX/1xyfy4pVjKqxruJqWX10IDuzeHjOjdXoa7VqG/s5XTepT9jpDczty8eiedG2bQUazVF798ThOCuuELLrpJB6enl82f+e0gfz7mPL/hTYZofPYB2S345Hp+fzs9GPo0iajQs/+4QuGcsvp/cvmT+rflWNz2vHFL6fwwA+GcPaQHNpkNGPWyX0Z2L3m/9ExfTI55ZjyD7+nLh1RY91oU9AniLp2Gbu0SeepS0Y26LWz2rXg5tP7c1xO+UG+W88cUOtzurWrvbc4rm8mk32P7ZJxvSJqx2XjjyqbPmdo7cNEQ47syNo5BVwwIheAD26cVGub75w2kGd+NLLsHzcSFjaaMm/WiRSE9cbyahguqs0tZxzD57edWuPjM8cexS1nlH9wDc3twItXjikbAgo3vFcn3r5uPGf5S9iWhvbLV42lZ+dWrJ1TQI9OLcvq//b8wVxzch9mjC4Psdu+cyzvXT+xwut2aZvBvFkncvNp/XllVs0fZvURvmdxZKeW9MpsXeMHWG2evDSy9/dZg7N569rxnNCramfhiHYZDO5R9QOjTXoak47uwtlDcrixoD9PXjKCn5zSl27ty/cOJh7dlQx/bCR8CLBdy2ZcOKon78yewJrbp5SVp6QYkwccgYW9kSaG7T3MOqkPr10zjldmjeXqSX3IP7JD2V7oHy4aypAj6+4gRYuCPgZKd+XrMx561/cG1vr4+zdOIiXFuOec4+vVlrVzCmjXItQTmnv5aOZ8N9RT7ndE7UFWenbDhSNzeSSsRwSw5vYpNEtNoXvHlqydU8CEflV3jR/4wZAqZT85pR9r5xSwdk5BhXBb/ovJQGg4qH9WW2af2q/KczPbpJeFeAffswt39pAcBvXoUHYlwNJeY/hxh/Drfg/s3p41txfwyqyxvHjlmBr+CiGDe7RniO9tRnMM9r/PDm3z/zl3EGvnFPC3S0YwMKcduZ1DAZ7ToTzIf3X2QNbOKajxm5Rd22Zw+YQ8zKys09AsNYUj2mUw7+qxvHRVxXW8eHRPenWuX8++8tlAE/t1oW/XNtz2ndAH8IhenXiyls5Ih5ah8ByTl1n2Plg7p4B7zjm+wvBKm/TaP6zNjO4dW9b4+FmDs8nr0pofDj+yrGzJz0/h4elDy+bzcztyWS3DiWP6hAK5Z+fyYc/s9i0qhHpNXrpqDPOuHssVE/Po2bkVvbu04cpJoW1zdFZb1s4pqDLOH2uN8/3bAMjr0pqnfjSS4275Z511Z4zuya59xfzbmF7c+c/PAUhLMYpLKp4cfnL/rvTLasuQIzsw6qjqhzLeu34i7cOC7YyB3fh80y4y0lJplpbCWYNzuP2FZTz90XpG9e7ElRP70KVNOu+s3sJx2VV3I78/tDsjj+pMj04tueOsY8nr2oY9+4tZun4nEOoZp1ioR/v2dePJateC1JSKb+7q3uxzLx/FkvU7uPGZpQBMHnAE/bPa8tnGndUeNAX4xZkDGNS9PRnNUnn3+gl0apVe655NwbFZLP96F5ecWL5n8OAPh1QYLik9KHbd5H4s/HIbV07MY8/+Yv7+8QbunDaQJxcVAnD/+YMB6N2l5g+8966fSIdWzUhPS2XvgWLunb+K8yI4YD0mrzOL123nzWvHY1T9Ww3IbsvufcW0qhRoQ3M78mzY2G8k/jRjGDu/La5Q9tx/juatlVvK5mvaOyndjOP6ZnJS/65l2y6rXQYbd+yjR8eWfLV1LwA/P+MYpo/MJXf287RJT2PX/mLyurbmjrOPA+Cta8eT3b4FKWHvlaP8h+2IXp1494tv6N+tLW9PGl/l2MLU47OZenzoGMaHN51Es1TjpLveJDuCg6nVKd1rORyl262+B9EB+h1R/72ZWDOXAN9Myc/PdwsXLqz380bcPp+NO/bV+3nXTu5L6/Q0bn72U1o0S+XpH40kNcW455WVPL9kIytvO5UHXl/Nr+eFQvrm0/pzsd8lds5x07NLuXBkLt978D227jnA9/JzSE1J4cqJeXz5zZ4Ku5TTf/c+Zw7qxstLN/HSp1+z+pdTqgRnuE8Kt3PGfe9w7rDuXDAiN6Jd4H0HD/H6is1MHlD36WSH465/rmDX/mJ+dnr14+YAS9fvoEXzVI7KbM3WPQf4dMMOxjTSFfoADhSXMH/Zpiq71KWeWlTIc59s4PcXVT0fe8XXu7jssQ956tKRZXtBySR39vMAvH/DRFYV7eaYrHZ8+NU29hwoZsqArLIQd87xwpKvmTzgiFrfy6VKShwvLo28fiLYX3yIs+7/Fz8t6M/waoaIEoWZLXLO5ddZLxZBb2aTgXuAVOBh59yc2uo3NOiLD5XwzEfr2VdcQtuMNIbmdqR5WgqpZlz+lw+57cxjSU0x9heXRHTd5/3Fh9iy+wDZ7VtQUuJYt20vR3aK7IyVSF77m90HKowJ1uTLb/bQo2PLiHYTRaJlz/5i9uwvpkuCn80j5eIW9GaWCnwOnAQUAh8A5zrnPqvpOQ0NehGRZBZp0MfiYOwwYJVz7gvn3AHgr8DUGCxHREQiEIugzwbWhc0X+rIKzGymmS00s4VFRUUxaIaIiEBsgr66geUq40POuYecc/nOufzMzMS+ToSISFMWi6AvBLqHzecAG2KwHBERiUAsgv4DIM/MeppZc+AcYG4MliMiIhGI+hemnHPFZnY58DKh0yt/55z7NNrLERGRyMTkm7HOuReAF2Lx2iIiUj+61o2ISMAlxCUQzKwI+LKBT+8MbKmzVrBonZOD1jk5HM46H+mcq/O0xYQI+sNhZgsj+WZYkGidk4PWOTk0xjpr6EZEJOAU9CIiAReEoH8o3g2IA61zctA6J4eYr3OTH6MXEZHaBaFHLyIitVDQi4gEXJMOejObbGYrzGyVmc2Od3vqw8y6m9lrZrbMzD41syt9eUczm2dmK/3vDr7czOxev66fmNngsNea7uuvNLPpYeVDzGyJf869liC3rDKzVDP7yMye8/M9zWyBb//j/hpJmFm6n1/lH88Ne43rffkKMzslrDzh3hNm1t7MnjSz5X57jwj6djazq/37eqmZ/cXMMoK2nc3sd2a22cyWhpXFfLvWtIxaOeea5A+h6+isBnoBzYHFQP94t6se7c8CBvvpNoTuytUf+BUw25fPBu7w01OAFwldBno4sMCXdwS+8L87+OkO/rH3gRH+OS8Cp8Z7vX27ZgGPAc/5+SeAc/z0A8ClfvpHwAN++hzgcT/d32/vdKCnfx+kJup7AngU+Dc/3RxoH+TtTOj+E2uAFmHb98KgbWdgLDAYWBpWFvPtWtMyam1rvP8JDuOPPAJ4OWz+euD6eLfrMNbnWUK3X1wBZPmyLGCFn36Q0C0ZS+uv8I+fCzwYVv6gL8sCloeVV6gXx/XMAeYDE4Dn/Jt4C5BWebsSujDeCD+d5utZ5W1dWi8R3xNAWx96Vqk8sNuZ8psPdfTb7TnglCBuZyCXikEf8+1a0zJq+2nKQzcR3cmqKfC7qoOABUBX59xGAP+7i69W0/rWVl5YTXm8/Qa4Fijx852A7c65Yj8f3s6ydfOP7/D16/u3iKdeQBHwez9c9bCZtSLA29k5tx64E/gK2Ehouy0i2Nu5VGNs15qWUaOmHPQR3ckq0ZlZa+Ap4Crn3M7aqlZT5hpQHjdmdhqw2Tm3KLy4mqqujseazDoT6qEOBu53zg0C9hDa3a5Jk19nP2Y8ldBwSzegFXBqNVWDtJ3rEtd1bMpB3+TvZGVmzQiF/P855572xZvMLMs/ngVs9uU1rW9t5TnVlMfTKOAMM1tL6KbxEwj18NubWekls8PbWbZu/vF2wFbq/7eIp0Kg0Dm3wM8/SSj4g7ydJwFrnHNFzrmDwNPASIK9nUs1xnataRk1aspB36TvZOWPoD8CLHPO3RX20Fyg9Mj7dEJj96XlF/ij98OBHX637WXgZDPr4HtSJxMav9wI7DKz4X5ZF4S9Vlw45653zuU453IJba9XnXPnA68BZ/tqlde59G9xtq/vfPk5/myNnkAeoQNXCfeecM59Dawzs76+aCLwGQHezoSGbIabWUvfptJ1Dux2DtMY27WmZdQsngdtonAgZAqhs1VWAzfGuz31bPtoQrtinwAf+58phMYm5wMr/e+Ovr4Bv9wf3t8AAACjSURBVPXrugTID3uti4FV/ueisPJ8YKl/zn1UOiAY5/UfR/lZN70I/QOvAv4GpPvyDD+/yj/eK+z5N/r1WkHYWSaJ+J4AjgcW+m39d0JnVwR6OwM/B5b7dv2J0JkzgdrOwF8IHYM4SKgHPqMxtmtNy6jtR5dAEBEJuKY8dCMiIhFQ0IuIBJyCXkQk4BT0IiIBp6AXEQk4Bb2ISMAp6EVEAu7/A6SijxMjKxrLAAAAAElFTkSuQmCC\n"
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -408,11 +452,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "From this graph, it is not possible to tell anything, because due to the nature of stochastic training process the length of training sessions varies greatly. To make more sense of this graph, we can calculate **running average** over series of experiments, let's say 100. This can be done conveniently using `np.convolve`:"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -420,25 +464,27 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
       ]
      },
+     "execution_count": 22,
      "metadata": {},
-     "execution_count": 22
+     "output_type": "execute_result"
     },
     {
-     "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 139.412611 105.274223 \r\nL 139.504011 98.578271 \r\nL 139.531431 101.342544 \r\nL 139.537525 103.689454 \r\nL 139.583225 95.318002 \r\nL 139.635018 99.431143 \r\nL 139.708139 97.132623 \r\nL 139.671579 102.328488 \r\nL 139.732512 100.683231 \r\nL 139.897033 117.268868 \r\nL 139.915313 114.323133 \r\nL 139.988434 119.36778 \r\nL 140.006714 116.966432 \r\nL 140.061554 118.956466 \r\nL 140.0189 115.944195 \r\nL 140.070694 118.732663 \r\nL 140.092021 124.757205 \r\nL 140.165141 117.438233 \r\nL 140.186468 121.702593 \r\nL 140.414969 105.165345 \r\nL 140.192561 122.355856 \r\nL 140.454576 107.82679 \r\nL 140.472856 111.964126 \r\nL 140.49723 105.364954 \r\nL 140.56121 105.588757 \r\nL 140.667844 112.079052 \r\nL 140.680031 109.756337 \r\nL 140.740964 115.284883 \r\nL 140.789711 110.754379 \r\nL 140.838458 110.808817 \r\nL 140.908532 106.411385 \r\nL 140.911579 106.598896 \r\nL 140.954232 102.927313 \r\nL 140.963372 103.151116 \r\nL 141.088286 92.571875 \r\nL 141.09438 94.235278 \r\nL 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284.011235 86.958647 \r\nL 284.075215 81.266785 \r\nL 284.148335 78.520659 \r\nL 284.093495 83.522964 \r\nL 284.190989 79.379579 \r\nL 284.200129 80.359475 \r\nL 284.264109 73.082844 \r\nL 284.367697 66.241722 \r\nL 284.379883 69.804429 \r\nL 284.501751 75.091025 \r\nL 284.41949 67.130887 \r\nL 284.507844 74.782539 \r\nL 284.584011 73.536499 \r\nL 284.550498 77.788762 \r\nL 284.623618 74.340981 \r\nL 284.626665 74.328884 \r\nL 284.629711 74.764393 \r\nL 284.663225 78.145637 \r\nL 284.730252 73.137283 \r\nL 284.733299 73.282452 \r\nL 284.846026 64.711392 \r\nL 284.748532 74.939806 \r\nL 284.900866 67.45147 \r\nL 284.910006 70.493985 \r\nL 284.97094 60.326058 \r\nL 284.99836 63.501645 \r\nL 285.02578 59.775623 \r\nL 285.101947 64.142811 \r\nL 285.108041 63.28389 \r\nL 285.150694 66.907084 \r\nL 285.181161 61.312002 \r\nL 285.220768 64.118616 \r\nL 285.330448 74.4922 \r\nL 285.357869 72.604994 \r\nL 285.379195 70.058476 \r\nL 285.427942 79.313043 \r\nL 285.430989 78.986411 \r\nL 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\n"
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -449,13 +495,13 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Varying Hyperparameters and Seeing the Result in Action\n",
     "\n",
     "Now it would be interesting to actually see how the trained model behaves. Let's run the simulation, and we will be following the same action selection strategy as during training: sampling according to the probability distribution in Q-Table: "
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -475,14 +521,14 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "\n",
     "## Saving result to an animated GIF\n",
     "\n",
     "If you want to impress your friends, you may want to send them the animated GIF picture of the balancing pole. To do this, we can invoke `env.render` to produce an image frame, and then save those to animated GIF using PIL library:"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -490,8 +536,8 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "360\n"
      ]
@@ -516,5 +562,32 @@
     "print(i)"
    ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}