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Update unit1.ipynb for Vectorized Monitor #470

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Update unit1.ipynb for Vectorized Monitor #470

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8 changes: 7 additions & 1 deletion notebooks/unit1/unit1.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -730,6 +730,7 @@
"source": [
"## Evaluate the agent 📈\n",
"- Remember to wrap the environment in a [Monitor](https://stable-baselines3.readthedocs.io/en/master/common/monitor.html).\n",
"- If you used a vectorized environment since the beginning, you will need a [Vectorized monitor](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html#multiprocessing-unleashing-the-power-of-vectorized-environments) to wrap the environment. You can search for 'VecMonitor' on the documentation. \n",
"- Now that our Lunar Lander agent is trained 🚀, we need to **check its performance**.\n",
"- Stable-Baselines3 provides a method to do that: `evaluate_policy`.\n",
"- To fill that part you need to [check the documentation](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html#basic-usage-training-saving-loading)\n",
Expand Down Expand Up @@ -778,7 +779,12 @@
"#@title\n",
"eval_env = Monitor(gym.make(\"LunarLander-v2\"))\n",
"mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)\n",
"print(f\"mean_reward={mean_reward:.2f} +/- {std_reward}\")"
"print(f\"mean_reward={mean_reward:.2f} +/- {std_reward}\")",
"# Alternative version for vectorized environments\n",
"eval_env = make_vec_env('LunarLander-v2', n_envs=16)",
"eval_env = VecMonitor(venv=eval_env)"
# Load the trained model
model = model.load("ppo-LunarLander-v2", env=eval_env, print_system_info=True)
]
},
{
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