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import os
import torch as T
import torch.nn.functional as F
import numpy as np
from buffer import ReplayBuffer
from networks import ActorNetwork, CriticNetwork
class Agent:
def __init__(self, actor_learning_rate, critic_learning_rate, env, input_dims, tau, gamma=0.99, update_actor_interval=2, warmup=1000, n_actions=2, max_size=1000000, layer1_size=256, layer2_size=128, batch_size=256, noise=0.1):
self.alpha = actor_learning_rate
self.beta = critic_learning_rate
self.env = env
self.input_dims = input_dims
self.tau = tau
self.gamma = gamma
self.max_action = env.action_space.high
self.min_action = env.action_space.low
self.memory = ReplayBuffer(max_size, input_dims, n_actions)
self.learn_step_cntr = 0
self.time_step = 0
self.update_actor_interval = update_actor_interval
self.warmup = warmup
self.n_actions = n_actions
self.batch_size = batch_size
self.noise = noise
#creat the networks
self.actor = ActorNetwork(learning_rate=actor_learning_rate, input_dims=input_dims, n_actions=n_actions,fc1_dims=layer1_size, fc2_dims=layer2_size, name='actor')
self.critic_1 = CriticNetwork(learning_rate=critic_learning_rate, input_dims=input_dims, n_actions=n_actions,fc1_dims=layer1_size, fc2_dims=layer2_size, name='critic_1')
self.critic_2 = CriticNetwork(learning_rate=critic_learning_rate, input_dims=input_dims, n_actions=n_actions,fc1_dims=layer1_size, fc2_dims=layer2_size, name='critic_2')
#create the target networks
self.target_actor = ActorNetwork(learning_rate=actor_learning_rate, input_dims=input_dims, n_actions=n_actions,fc1_dims=layer1_size, fc2_dims=layer2_size, name='target_actor')
self.target_critic_1 = CriticNetwork(learning_rate=critic_learning_rate, input_dims=input_dims, n_actions=n_actions,fc1_dims=layer1_size, fc2_dims=layer2_size, name='target_critic_1')
self.target_critic_2 = CriticNetwork(learning_rate=critic_learning_rate, input_dims=input_dims, n_actions=n_actions,fc1_dims=layer1_size, fc2_dims=layer2_size, name='target_critic_2')
self.noise = noise
self.update_network_parameters(tau=1)
def choose_action(self, observation, validation=False):
if self.time_step < self.warmup and validation is False:
mu = T.tensor(np.random.normal(scale=self.noise, size=(self.n_actions,)), dtype=T.float).to(self.actor.device)
action = mu
else:
state = T.tensor(observation, dtype=T.float).to(self.actor.device)
nu = self.actor.forward(state).to(self.actor.device)
if validation:
nu_price = nu
else:
nu_price = nu + T.tensor(np.random.normal(scale=self.noise, size=(self.n_actions,)), dtype=T.float).to(self.actor.device)
nu_price = T.clamp(nu_price, self.min_action[0], self.max_action[0])
action = nu_price
self.time_step += 1
return action.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_ctr < self.batch_size*10:
return
states, actions, rewards, next_states, dones = self.memory.sample_buffer(self.batch_size)
next_states = T.tensor(next_states, dtype=T.float).to(self.critic_1.device)
states = T.tensor(states, dtype=T.float).to(self.critic_1.device)
actions = T.tensor(actions, dtype=T.float).to(self.critic_1.device)
rewards = T.tensor(rewards, dtype=T.float).to(self.critic_1.device)
dones = T.tensor(dones).to(self.critic_1.device)
target_actions = self.target_actor.forward(next_states)
target_actions = target_actions + T.clamp(T.tensor(np.random.normal(scale=0.2)), -0.5, 0.5)
target_actions = T.clamp(target_actions, self.min_action[0], self.max_action[0])
next_q1 = self.target_critic_1.forward(next_states, target_actions)
next_q2 = self.target_critic_2.forward(next_states, target_actions)
q1 = self.critic_1.forward(states, actions)
q2 = self.critic_2.forward(states, actions)
next_q1[dones] = 0.0
next_q2[dones] = 0.0
next_q1 = next_q1.view(-1)
next_q2 = next_q2.view(-1)
next_critic_value = T.min(next_q1, next_q2)
target = rewards + self.gamma*next_critic_value
target = target.view(self.batch_size, 1)
self.critic_1.optimizer.zero_grad()
self.critic_2.optimizer.zero_grad()
q1_loss = F.mse_loss(target, q1)
q2_loss = F.mse_loss(target, q2)
critic_loss = q1_loss + q2_loss
critic_loss.backward()
self.critic_1.optimizer.step()
self.critic_2.optimizer.step()
self.learn_step_cntr += 1
if self.learn_step_cntr % self.update_actor_interval != 0:
return
self.actor.optimizer.zero_grad()
actor_q1_loss = self.critic_1.forward(states, self.actor.forward(states))
actor_loss = -T.mean(actor_q1_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_1_params = self.critic_1.named_parameters()
critic_2_params = self.critic_2.named_parameters()
target_actor_params = self.target_actor.named_parameters()
target_critic_1_params = self.target_critic_1.named_parameters()
target_critic_2_params = self.target_critic_2.named_parameters()
actor_state_dict = dict(actor_params)
critic_1_state_dict = dict(critic_1_params)
critic_2_state_dict = dict(critic_2_params)
target_actor_state_dict = dict(target_actor_params)
target_critic_1_state_dict = dict(target_critic_1_params)
target_critic_2_state_dict = dict(target_critic_2_params)
for name in critic_1_state_dict:
critic_1_state_dict[name] = tau*critic_1_state_dict[name].clone() + (1-tau)*target_critic_1_state_dict[name].clone()
for name in critic_2_state_dict:
critic_2_state_dict[name] = tau*critic_2_state_dict[name].clone() + (1-tau)*target_critic_2_state_dict[name].clone()
for name in actor_state_dict:
actor_state_dict[name] = tau*actor_state_dict[name].clone() + (1-tau)*target_actor_state_dict[name].clone()
self.target_critic_1.load_state_dict(critic_1_state_dict)
self.target_critic_2.load_state_dict(critic_2_state_dict)
self.target_actor.load_state_dict(actor_state_dict)
def save_models(self):
self.actor.save_checkpoint()
self.critic_1.save_checkpoint()
self.critic_2.save_checkpoint()
self.target_actor.save_checkpoint()
self.target_critic_1.save_checkpoint()
self.target_critic_2.save_checkpoint()
def load_models(self):
try:
self.actor.load_checkpoint()
self.critic_1.load_checkpoint()
self.critic_2.load_checkpoint()
self.target_actor.load_checkpoint()
self.target_critic_1.load_checkpoint()
self.target_critic_2.load_checkpoint()
print("\nModels loaded successfully\n")
self.time_step = self.warmup + 100
except Exception as e:
print(f"\nFailed to load models. Starting from scratch. Error: {e}\n")