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myddpg_torch.py
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myddpg_torch.py
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import os
import torch as T
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
class OUActionNoise(object):
def __init__(self, mu, sigma=0.15, theta=0.2, dt=1e-2, x0=None):
self.theta = theta
self.mu = mu
self.sigma = sigma
self.dt = dt
self.x0 = x0
self.reset()
def __call__(self):
x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + self.sigma * np.sqrt(self.dt) * np.random.normal(size=self.mu.shape)
self.x_prev = x
return x
def reset(self):
self.x_prev = self.x0 if self.x0 is not None else np.zeros_like(self.mu)
class ReplayBuffer(object):
def __init__(self,max_size,input_shape,n_actions):
self.mem_size = max_size
self.mem_cntr = 0
self.state_memory = np.zeros((self.mem_size, *input_shape))
self.new_state_memory = np.zeros((self.mem_size, *input_shape))
self.action_memory = np.zeros((self.mem_size, n_actions))
self.reward_memory = np.zeros(self.mem_size)
self.terminal_memory = np.zeros(self.mem_size, dtype=np.float32)
def store_transition(self, state, action, reward, state_, done):
index = self.mem_cntr % self.mem_size
self.state_memory[index] = state
self.new_state_memory[index] = state_
self.reward_memory[index] = reward
self.action_memory[index] = action
self.terminal_memory[index] = 1 - int(done)
self.mem_cntr += 1
def sample_buffer(self,batch_size):
max_mem = min(self.mem_cntr,self.mem_size)
batch = np.random.choice(max_mem, batch_size)
states = self.state_memory[batch]
new_states = self.new_state_memory[batch]
actions = self.action_memory[batch]
rewards = self.reward_memory[batch]
terminal = self.terminal_memory[batch]
return states, actions, rewards, new_states, terminal
class CriticNetwork(nn.Module):
def __init__(self,beta,input_dims,fc1_dims,fc2_dims,n_actions,name,chkpt_dir='tmp/ddpg'):
super(CriticNetwork, self).__init__()
self.input_dims = input_dims
self.fc1_dims = fc1_dims
self.fc2_dims = fc2_dims
self.n_actions = n_actions
self.checkpoint_file = os.path.join(chkpt_dir,name+'_ddpg')
self.fc1 = nn.Linear(*self.input_dims, self.fc1_dims)
f1 = 1 / np.sqrt(self.fc1.weight.data.size()[0])
T.nn.init.uniform_(self.fc1.weight.data, -f1,f1)
T.nn.init.uniform_(self.fc1.bias.data,-f1,f1)
self.bn1 = nn.LayerNorm(self.fc1_dims)
self.fc2 = nn.Linear(self.fc1_dims,self.fc2_dims)
f2 = 1 / np.sqrt(self.fc2.weight.data.size()[0])
T.nn.init.uniform_(self.fc2.weight.data,-f2,f2)
T.nn.init.uniform_(self.fc2.bias.data,-f2,f2)
self.bn2 = nn.LayerNorm(self.fc2_dims)
self.action_value = nn.Linear(self.n_actions, fc2_dims)
f3 = .003
self.q = nn.Linear(self.fc2_dims, 1)
T.nn.init.uniform_(self.q.weight.data, -f3,f3)
T.nn.init.uniform_(self.q.bias.data, -f3,f3)
self.optimizer = optim.Adam(self.parameters(), lr=beta)
self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu')
self.to(self.device)
def forward(self,state,action):
state_value = self.fc1(state)
state_value = self.bn1(state_value)
state_value = F.relu(state_value)
state_value = self.fc2(state_value)
state_value = self.bn2(state_value)
action_value = F.relu(self.action_value(action))
state_action_value = F.relu(T.add(state_value, action_value)) # double relu mad sketch yo
state_action_value = self.q(state_action_value)
return state_action_value
def save_checkpoint(self):
print('... saving checkpoint ...')
T.save(self.state_dict(), self.checkpoint_file)
def load_checkpoint(self):
print(' ... loading checkpoint ...')
self.load_state_dict(T.load(self.checkpoint_file))
class ActorNetwork(nn.Module):
def __init__(self,alpha,input_dims,fc1_dims,fc2_dims,n_actions,name,chkpt_dir='tmp/ddpg'):
super(ActorNetwork,self).__init__()
self.input_dims = input_dims
self.n_actions = n_actions
self.fc1_dims = fc1_dims
self.fc2_dims = fc2_dims
self.checkpoint_file = os.path.join(chkpt_dir,name+'_ddpg')
self.fc1 = nn.Linear(*self.input_dims, self.fc1_dims)
f1 = 1 / np.sqrt(self.fc1.weight.data.size()[0])
T.nn.init.uniform_(self.fc1.weight.data, -f1,f1)
T.nn.init.uniform_(self.fc1.bias.data,-f1,f1)
self.bn1 = nn.LayerNorm(self.fc1_dims)
self.fc2 = nn.Linear(self.fc1_dims,self.fc2_dims)
f2 = 1 / np.sqrt(self.fc2.weight.data.size()[0])
T.nn.init.uniform_(self.fc2.weight.data,-f2,f2)
T.nn.init.uniform_(self.fc2.bias.data,-f2,f2)
self.bn2 = nn.LayerNorm(self.fc2_dims)
f3 = .003
self.mu = nn.Linear(self.fc2_dims, self.n_actions)
T.nn.init.uniform_(self.mu.weight.data,-f3,f3)
T.nn.init.uniform_(self.mu.bias.data,-f3,f3)
self.optimizer = optim.Adam(self.parameters(), lr=alpha)
self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu')
self.to(self.device)
def forward(self,state):
gaming = self.fc1(state)
gaming = self.bn1(gaming)
gaming = F.relu(gaming)
gaming = self.fc2(gaming)
gaming = self.bn2(gaming)
gaming = F.relu(gaming)
gaming = T.tanh(self.mu(gaming))
return gaming
def save_checkpoint(self):
print('... saving checkpoint ...')
T.save(self.state_dict(), self.checkpoint_file)
def load_checkpoint(self):
print(' ... loading checkpoint ...')
self.load_state_dict(T.load(self.checkpoint_file))
class Agent(object):
def __init__ (self,alpha,beta,input_dims,tau,env,gamma=0.99,n_actions=2,max_size=1000000,layer1_size=400,layer2_size=300,batch_size=64):
self.gamma = gamma
self.tau = tau
self.batch_size = batch_size
self.memory = ReplayBuffer(max_size, input_dims, n_actions)
self.actor = ActorNetwork(alpha,input_dims, layer1_size,layer2_size,n_actions,'Actor')
self.critic = CriticNetwork(alpha,input_dims, layer1_size,layer2_size,n_actions,'Critic')
self.target_actor = ActorNetwork(alpha,input_dims, layer1_size,layer2_size,n_actions,'TargetActor')
self.target_critic = CriticNetwork(alpha,input_dims, layer1_size,layer2_size,n_actions,'TargetCritic')
self.noise = OUActionNoise(mu=np.zeros(n_actions))
self.update_network_parameters(tau=1)
def choose_action(self,observation):
self.actor.eval()
observation = T.tensor(observation, dtype=T.float).to(self.actor.device)
mu = self.actor(observation).to(self.actor.device)
mu_prime = mu + T.tensor(self.noise(),dtype=T.float).to(self.actor.device)
self.actor.train()
return mu_prime.cpu().detach().numpy()
def remember(self,state,action,reward,new_state,done):
self.memory.store_transition(state,action,reward,new_state,done)
def learn(self):
if self.memory.mem_cntr < self.batch_size:
return
state,action,reward,new_state, done = self.memory.sample_buffer(self.batch_size)
reward = T.tensor(reward,dtype=T.float).to(self.critic.device)
done = T.tensor(done,dtype=T.float).to(self.critic.device)
new_state = T.tensor(new_state,dtype=T.float).to(self.critic.device)
action = T.tensor(action,dtype=T.float).to(self.critic.device)
state = T.tensor(state,dtype=T.float).to(self.critic.device)
self.target_actor.eval()
self.target_critic.eval()
self.critic.eval()
target_actions = self.target_actor.forward(new_state)
critic_value_ = self.target_critic.forward(new_state,target_actions)
critic_value = self.critic.forward(state,action)
target = []
for j in range(self.batch_size):
target.append(reward[j] + self.gamma*critic_value_[j]*done[j])
target = T.tensor(target).to(self.critic.device)
target = target.view(self.batch_size,1)
self.critic.train()
self.critic.optimizer.zero_grad()
critic_loss = F.mse_loss(target,critic_value)
critic_loss.backward()
self.critic.optimizer.step()
self.critic.eval()
self.actor.optimizer.zero_grad()
mu = self.actor.forward(state)
self.actor.train()
actor_loss = -self.critic.forward(state,mu)
actor_loss = T.mean(actor_loss)
actor_loss.backward()
self.actor.optimizer.step()
self.update_network_parameters()
def update_network_parameters(self,tau=None):
if tau is None:
tau = self.tau
actor_params = self.actor.named_parameters()
critic_params = self.critic.named_parameters()
target_actor_params = self.target_actor.named_parameters()
target_critic_params = self.target_critic.named_parameters()
critic_state_dict = dict(critic_params)
actor_state_dict = dict(actor_params)
target_critic_dict = dict(target_critic_params)
target_actor_dict = dict(target_actor_params)
for name in critic_state_dict:
critic_state_dict[name] = tau*critic_state_dict[name].clone() + (1-tau)*target_critic_dict[name].clone()
self.target_critic.load_state_dict(critic_state_dict)
for name in actor_state_dict:
actor_state_dict[name] = tau*actor_state_dict[name].clone() + (1-tau)*target_actor_dict[name].clone()
self.target_actor.load_state_dict(actor_state_dict)
def save_models(self):
self.actor.save_checkpoint()
self.critic.save_checkpoint()
self.target_actor.save_checkpoint()
self.target_critic.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.critic.load_checkpoint()
self.target_actor.load_checkpoint()
self.target_critic.load_checkpoint()