class Agent(): def __init__(self, alpha, beta, input_dims, tau, n_actions, gamma=0.99, max_size=50000, fc1_dims=400, fc2_dims=300, batch_size=32): self.gamma = gamma self.tau = tau self.batch_size = batch_size self.alpha = alpha self.beta = beta self.memory = ReplayBuffer(max_size, input_dims, n_actions) self.noise = OUActionNoise(mu=np.zeros(n_actions)) self.actor = ActorNetwork(alpha, input_dims, fc1_dims, fc2_dims, n_actions=n_actions, name='actor') self.critic = CriticNetwork(beta, input_dims, fc1_dims, fc2_dims, n_actions=n_actions, name='critic') self.target_actor = ActorNetwork(alpha, input_dims, fc1_dims, fc2_dims, n_actions=n_actions, name='target_actor') self.target_critic = CriticNetwork(beta, input_dims, fc1_dims, fc2_dims, n_actions=n_actions, name='target_critic') self.update_network_parameters( tau=1) # for the first time target_actor and actor are same def choose_action(self, observation): self.actor.eval( ) # we are setting our actor network to eval mode because we have batch normalization layer # and we dont want to calculate statistics for that layer at this step state = T.tensor([observation], dtype=T.float).to(self.actor.device) mu = self.actor.forward(state).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()[0] def remember(self, state, action, reward, state_, done): self.memory.store_transition(state, action, reward, state_, done) def save_models(self): self.actor.save_checkpoint() self.target_actor.save_checkpoint() self.critic.save_checkpoint() self.target_critic.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.target_actor.load_checkpoint() self.critic.load_checkpoint() self.target_critic.load_checkpoint() def learn(self): if self.memory.mem_cntr < self.batch_size: return states, actions, rewards, states_, done = self.memory.sample_buffer( self.batch_size) states = T.tensor(states, dtype=T.float).to(self.actor.device) states_ = T.tensor(states_, dtype=T.float).to(self.actor.device) actions = T.tensor(actions, dtype=T.float).to(self.actor.device) rewards = T.tensor(rewards, dtype=T.float).to(self.actor.device) done = T.tensor(done).to(self.actor.device) target_actions = self.target_actor.forward(states_) critic_value_ = self.target_critic.forward(states_, target_actions) critic_value = self.critic.forward(states, actions) critic_value_[done] = 0.0 critic_value_ = critic_value_.view(-1) target = rewards + self.gamma * critic_value_ target = target.view(self.batch_size, 1) self.critic.optimizer.zero_grad() critic_loss = F.mse_loss(target, critic_value) critic_loss.backward() self.critic.optimizer.step() self.actor.optimizer.zero_grad() actor_loss = -self.critic.forward(states, self.actor.forward(states)) actor_loss = T.mean(actor_loss) actor_loss.backward() self.actor.optimizer.step() self.update_network_parameters( ) # sending tau None so that local tau variable there takes the value of class tau variable 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_state_dict = dict(target_critic_params) target_actor_state_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_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.load_state_dict(critic_state_dict) self.target_actor.load_state_dict(actor_state_dict)
class Agent: def __init__(self, actor_dims, critic_dims, n_actions, n_agents, agent_idx, chkpt_dir, alpha=0.01, beta=0.01, fc1=64, fc2=64, gamma=0.95, tau=0.01): self.gamma = gamma self.tau = tau self.n_actions = n_actions self.agent_name = 'agent_%s' % agent_idx self.actor = ActorNetwork(alpha, actor_dims, fc1, fc2, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_actor') self.critic = CriticNetwork(beta, critic_dims, fc1, fc2, n_agents, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_critic') self.target_actor = ActorNetwork(alpha, actor_dims, fc1, fc2, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_target_actor') self.target_critic = CriticNetwork(beta, critic_dims, fc1, fc2, n_agents, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_target_critic') self.update_network_parameters(tau=1) def choose_action(self, observation): state = T.tensor([observation], dtype=T.float).to(self.actor.device) actions = self.actor.forward(state) noise = T.rand(self.n_actions).to(self.actor.device) action = actions + noise return action.detach().cpu().numpy()[0] def update_network_parameters(self, tau=None): if tau is None: tau = self.tau target_actor_params = self.target_actor.named_parameters() actor_params = self.actor.named_parameters() target_actor_state_dict = dict(target_actor_params) actor_state_dict = dict(actor_params) 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_actor.load_state_dict(actor_state_dict) target_critic_params = self.target_critic.named_parameters() critic_params = self.critic.named_parameters() target_critic_state_dict = dict(target_critic_params) critic_state_dict = dict(critic_params) for name in critic_state_dict: critic_state_dict[name] = tau*critic_state_dict[name].clone() + \ (1-tau)*target_critic_state_dict[name].clone() self.target_critic.load_state_dict(critic_state_dict) def save_models(self): self.actor.save_checkpoint() self.target_actor.save_checkpoint() self.critic.save_checkpoint() self.target_critic.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.target_actor.load_checkpoint() self.critic.load_checkpoint() self.target_critic.load_checkpoint()
class Agent: def __init__(self, actor_dims, critic_dims, n_actions, n_agents, agent_idx, chkpt_dir, alpha=0.01, beta=0.01, fc1=64, fc2=64, gamma=0.95, tau=0.01): """ Args: actor_dims: critic_dims: n_actions: number of actions n_agents: agent_idx: agent index chkpt_dir: checkpoint directory alpha: learning rate beta: learning rate fc1: fc2: gamma: discount factor tau: soft update parameter """ self.gamma = gamma self.tau = tau self.n_actions = n_actions self.agent_name = 'agent_%s' % agent_idx # e.g., name = agent_1_actor self.actor = ActorNetwork(alpha, actor_dims, fc1, fc2, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_actor') self.critic = CriticNetwork(beta, critic_dims, fc1, fc2, n_agents, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_critic') self.target_actor = ActorNetwork(alpha, actor_dims, fc1, fc2, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_target_actor') self.target_critic = CriticNetwork(beta, critic_dims, fc1, fc2, n_agents, n_actions, chkpt_dir=chkpt_dir, name=self.agent_name + '_target_critic') # initially target networks and networks have the same parameters self.update_network_parameters(tau=1) def choose_action(self, observation): """ Args: observation: Returns: action w.r.t. the current policy and exploration """ state = T.tensor([observation], dtype=T.float).to(self.actor.device) # action of current policy actions = self.actor.forward(state) # exploration (0.1 is the parameter of the exploration) noise = 0.1 * T.rand(self.n_actions).to(self.actor.device) # print(f"action={action}, noise={noise}") action = actions + noise # action = actions return action.detach().cpu().numpy()[0] def update_network_parameters(self, tau=None): # use default tau if nothing is input if tau is None: tau = self.tau target_actor_params = self.target_actor.named_parameters() actor_params = self.actor.named_parameters() # soft update of target networks target_actor_state_dict = dict(target_actor_params) actor_state_dict = dict(actor_params) 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_actor.load_state_dict(actor_state_dict) target_critic_params = self.target_critic.named_parameters() critic_params = self.critic.named_parameters() target_critic_state_dict = dict(target_critic_params) critic_state_dict = dict(critic_params) for name in critic_state_dict: critic_state_dict[name] = tau * critic_state_dict[name].clone() + \ (1 - tau) * target_critic_state_dict[ name].clone() self.target_critic.load_state_dict(critic_state_dict) def save_models(self): self.actor.save_checkpoint() self.target_actor.save_checkpoint() self.critic.save_checkpoint() self.target_critic.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.target_actor.load_checkpoint() self.critic.load_checkpoint() self.target_critic.load_checkpoint()
class Agent(object): def __init__(self, alpha, beta, input_dims, action_bound, 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.memory = ReplayBuffer(max_size, input_dims, n_actions) self.batch_size = batch_size self.action_bound = action_bound self.actor = ActorNetwork(alpha, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='Actor') self.critic = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='Critic') self.target_actor = ActorNetwork(alpha, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='TargetActor') self.target_critic = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='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.forward(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 * T.tensor(self.action_bound)).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).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) """ #Verify that the copy assignment worked correctly target_actor_params = self.target_actor.named_parameters() target_critic_params = self.target_critic.named_parameters() critic_state_dict = dict(target_critic_params) actor_state_dict = dict(target_actor_params) print('\nActor Networks', tau) for name, param in self.actor.named_parameters(): print(name, T.equal(param, actor_state_dict[name])) print('\nCritic Networks', tau) for name, param in self.critic.named_parameters(): print(name, T.equal(param, critic_state_dict[name])) input() """ def save_models(self): self.actor.save_checkpoint() self.target_actor.save_checkpoint() self.critic.save_checkpoint() self.target_critic.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.target_actor.load_checkpoint() self.critic.load_checkpoint() self.target_critic.load_checkpoint() def check_actor_params(self): current_actor_params = self.actor.named_parameters() current_actor_dict = dict(current_actor_params) original_actor_dict = dict(self.original_actor.named_parameters()) original_critic_dict = dict(self.original_critic.named_parameters()) current_critic_params = self.critic.named_parameters() current_critic_dict = dict(current_critic_params) print('Checking Actor parameters') for param in current_actor_dict: print( param, T.equal(original_actor_dict[param], current_actor_dict[param])) print('Checking critic parameters') for param in current_critic_dict: print( param, T.equal(original_critic_dict[param], current_critic_dict[param])) input()
class Agent(): def __init__(self, input_dims, env, n_actions): self.memory = ReplayBuffer(input_dims) self.n_actions = n_actions self.actor_nn = ActorNetwork(input_dims, n_actions=n_actions, name=Constants.env_id + '_actor', max_action=env.action_space.n) self.critic_local_1_nn = CriticNetwork(input_dims, n_actions=n_actions, name=Constants.env_id + '_critic_local_1') self.critic_local_2_nn = CriticNetwork(input_dims, n_actions=n_actions, name=Constants.env_id + '_critic_local_2') self.critic_target_1_nn = CriticNetwork(input_dims, n_actions=n_actions, name=Constants.env_id + '_critic_target_1') self.critic_target_2_nn = CriticNetwork(input_dims, n_actions=n_actions, name=Constants.env_id + '_critic_target_2') def choose_action(self, observation): state = T.Tensor([observation]).to(Constants.device) _, max_probability_action = self.actor_nn.sample_action(state) return max_probability_action 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 < Hyper.batch_size: return state, action, reward, next_state, done = self.memory.sample_buffer() reward = T.tensor(reward, dtype=T.float).to(Constants.device) done = T.tensor(done).to(Constants.device) next_state = T.tensor(next_state, dtype=T.float).to(Constants.device) state = T.tensor(state, dtype=T.float).to(Constants.device) action = T.tensor(action, dtype=T.float).to(Constants.device) (action_probabilities, log_action_probabilities), _ = self.actor_nn.sample_action(next_state) with T.no_grad(): action_logits1 = self.critic_target_1_nn(next_state) q1_new_policy = T.argmax(action_logits1, dim=1, keepdim=True) action_logits2 = self.critic_target_2_nn(next_state) q2_new_policy = T.argmax(action_logits2, dim=1, keepdim=True) q_value = T.min(q1_new_policy, q2_new_policy) min_qf_next_target = action_probabilities * ( q_value - Hyper.alpha * log_action_probabilities) min_qf_next_target_sum = min_qf_next_target.sum( dim=1).unsqueeze(-1) not_done = (1.0 - done * 1).unsqueeze(-1) next_q_value = reward.unsqueeze( -1) + not_done * Hyper.gamma * (min_qf_next_target_sum) action_logits1 = self.critic_local_1_nn(state).gather(1, action.long()) q_value1 = action_logits1.sum(dim=1).unsqueeze(-1) action_logits2 = self.critic_local_2_nn(state).gather(1, action.long()) q_value2 = action_logits2.sum(dim=1).unsqueeze(-1) self.critic_local_1_nn.optimizer.zero_grad() self.critic_local_2_nn.optimizer.zero_grad() critic_1_loss = F.mse_loss(q_value1, next_q_value) critic_2_loss = F.mse_loss(q_value2, next_q_value) critic_1_loss.backward() critic_2_loss.backward() self.critic_local_1_nn.optimizer.step() self.critic_local_2_nn.optimizer.step() (action_probabilities, log_action_probabilities), _ = self.actor_nn.sample_action(state) # ------------------------------------------------------------------------------------------- # Calculates the loss for the actor. This loss includes the additional entropy term # CHANGE0003 Soft state-value where actions are discrete self.actor_nn.optimizer.zero_grad() action_logits1 = self.critic_target_1_nn(state) q1_new_policy = T.argmax(action_logits1, dim=1, keepdim=True) action_logits2 = self.critic_target_2_nn(state) q2_new_policy = T.argmax(action_logits2, dim=1, keepdim=True) q_value = T.min(q1_new_policy, q2_new_policy) inside_term = Hyper.alpha * log_action_probabilities - q_value policy_loss = (action_probabilities * inside_term).sum(dim=1).mean() policy_loss.backward(retain_graph=True) self.actor_nn.optimizer.step() self.update_q_weights() def update_q_weights(self): local_1_parameters = self.critic_local_1_nn.named_parameters() local_2_parameters = self.critic_local_2_nn.named_parameters() target_1_parameters = self.critic_target_1_nn.named_parameters() target_2_parameters = self.critic_target_2_nn.named_parameters() self.update_network_parameters_line(target_1_parameters, local_1_parameters, Hyper.tau) self.update_network_parameters_line(target_2_parameters, local_2_parameters, Hyper.tau) def update_network_parameters_line(self, target_params, local_params, tau): for target_param, local_param in zip(target_params, local_params): target_param[1].data.copy_(tau * local_param[1].data + (1.0 - tau) * target_param[1].data) def save_models(self): print('.... saving models ....') self.actor_nn.save_checkpoint() self.critic_local_1_nn.save_checkpoint() self.critic_local_2_nn.save_checkpoint() self.critic_target_1_nn.save_checkpoint() self.critic_target_2_nn.save_checkpoint() def load_models(self): print('.... loading models ....') self.actor_nn.load_checkpoint() self.critic_local_1_nn.load_checkpoint() self.critic_local_2_nn.load_checkpoint() self.critic_target_1_nn.load_checkpoint() self.critic_target_2_nn.load_checkpoint()
class Agent(): def __init__(self, alpha, beta, input_dims, tau, env, action_bound, gamma=0.99, update_actor_interval=2, warmup=1000, n_actions=2, max_size=1000000, layer1_size=400, layer2_size=300, batch_size=100, noise=0.1): self.gamma = gamma self.tau = tau self.max_action = env.action_space.high self.min_action = env.action_space.low self.memory = ReplayBuffer(max_size, input_dims, n_actions) self.batch_size = batch_size self.learn_step_cntr = 0 self.time_step = 0 self.warmup = warmup self.n_actions = n_actions self.update_actor_iter = update_actor_interval self.action_bound = action_bound self.actor = ActorNetwork(alpha, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='actor') self.critic_1 = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='critic_1') self.critic_2 = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='critic_2') self.target_actor = ActorNetwork(alpha, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='target_actor') self.target_critic_1 = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='target_critic_1') self.target_critic_2 = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='target_critic_2') self.noise = noise self.update_network_parameters(tau=1) def choose_action(self, observation): if self.time_step < self.warmup: mu = T.tensor( np.random.normal(scale=self.noise, size=(self.n_actions, ))) else: state = T.tensor(observation, dtype=T.float).to(self.actor.device) mu = self.actor.forward(state).to(self.actor.device) mu_prime = mu + T.tensor(np.random.normal(scale=self.noise), dtype=T.float).to(self.actor.device) mu_prime = T.clamp(mu_prime, self.min_action[0], self.max_action[0]) self.time_step += 1 return (mu_prime * T.tensor(self.action_bound)).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_1.device) done = T.tensor(done).to(self.critic_1.device) state_ = T.tensor(new_state, dtype=T.float).to(self.critic_1.device) state = T.tensor(state, dtype=T.float).to(self.critic_1.device) action = T.tensor(action, dtype=T.float).to(self.critic_1.device) target_actions = self.target_actor.forward(state_) 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]) q1_ = self.target_critic_1.forward(state_, target_actions) q2_ = self.target_critic_2.forward(state_, target_actions) q1 = self.critic_1.forward(state, action) q2 = self.critic_2.forward(state, action) q1_[done] = 0.0 q2_[done] = 0.0 q1_ = q1_.view(-1) q2_ = q2_.view(-1) critic_value_ = T.min(q1_, q2_) target = reward + self.gamma * 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_iter != 0: return self.actor.optimizer.zero_grad() actor_q1_loss = self.critic_1.forward(state, self.actor.forward(state)) 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() critic_1 = dict(critic_1_params) critic_2 = dict(critic_2_params) actor = dict(actor_params) target_actor = dict(target_actor_params) target_critic_1 = dict(target_critic_1_params) target_critic_2 = dict(target_critic_2_params) for name in critic_1: critic_1[name] = tau*critic_1[name].clone() + \ (1-tau)*target_critic_1[name].clone() for name in critic_2: critic_2[name] = tau*critic_2[name].clone() + \ (1-tau)*target_critic_2[name].clone() for name in actor: actor[name] = tau*actor[name].clone() + \ (1-tau)*target_actor[name].clone() self.target_critic_1.load_state_dict(critic_1) self.target_critic_2.load_state_dict(critic_2) self.target_actor.load_state_dict(actor) def save_models(self): self.actor.save_checkpoint() self.target_actor.save_checkpoint() self.critic_1.save_checkpoint() self.critic_2.save_checkpoint() self.target_critic_1.save_checkpoint() self.target_critic_2.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.target_actor.load_checkpoint() self.critic_1.load_checkpoint() self.critic_2.load_checkpoint() self.target_critic_1.load_checkpoint() self.target_critic_2.load_checkpoint()
class Agent(): def __init__(self, input_dims, env, n_actions): self.memory = ReplayBuffer(input_dims) self.n_actions = n_actions self.actor_nn = ActorNetwork(input_dims, n_actions=n_actions, name=Constants.env_id + '_actor', max_action=env.action_space.n) self.critic_local_1_nn = CriticNetwork(input_dims, n_actions=n_actions, name=Constants.env_id + '_critic_local_1') self.critic_local_2_nn = CriticNetwork(input_dims, n_actions=n_actions, name=Constants.env_id + '_critic_local_2') self.critic_target_1_nn = CriticNetwork(input_dims, n_actions=n_actions, name=Constants.env_id + '_critic_target_1') self.critic_target_2_nn = CriticNetwork(input_dims, n_actions=n_actions, name=Constants.env_id + '_critic_target_2') self.value_nn = ValueNetwork(input_dims, name=Constants.env_id + '_value') self.target_value_nn = ValueNetwork(input_dims, name=Constants.env_id + '_target_value') self.update_network_parameters(tau=1) def choose_action(self, observation): state = T.Tensor([observation]).to(Constants.device) _, max_probability_action = self.actor_nn.sample_action(state) return max_probability_action 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 < Hyper.batch_size: return state, action, reward, next_state, done = self.memory.sample_buffer() reward = T.tensor(reward, dtype=T.float).to(Constants.device) done = T.tensor(done).to(Constants.device) next_state = T.tensor(next_state, dtype=T.float).to(Constants.device) state = T.tensor(state, dtype=T.float).to(Constants.device) action = T.tensor(action, dtype=T.float).to(Constants.device) # value_from_nn = self.value_nn(state).view(-1) value_from_nn = self.value_nn(state) new_value_from_nn = self.target_value_nn(next_state).view(-1) new_value_from_nn[done] = 0.0 (action_probabilities, log_action_probabilities), _ = self.actor_nn.sample_action(next_state) with T.no_grad(): q1_new_policy = self.critic_target_1_nn(next_state) q2_new_policy = self.critic_target_2_nn(next_state) critic_value = T.min(q1_new_policy, q2_new_policy) self.value_nn.optimizer.zero_grad() # CHANGE0003 Soft state-value where actions are discrete inside_term = Hyper.alpha * log_action_probabilities - critic_value #value_target = action_probabilities * (critic_value - Hyper.alpha * log_action_probabilities) value_loss = (action_probabilities * inside_term).sum(dim=1).mean() value_loss.backward(retain_graph=True) self.value_nn.optimizer.step() (action_probabilities, log_action_probabilities), _ = self.actor_nn.sample_action(state) with T.no_grad(): q1_new_policy = self.critic_local_1_nn(state) q2_new_policy = self.critic_local_1_nn(state) critic_value = T.min(q1_new_policy, q2_new_policy) # CHANGE0005 Objective for policy actor_loss = action_probabilities * ( Hyper.alpha * log_action_probabilities - critic_value) actor_loss = T.mean(actor_loss) self.actor_nn.optimizer.zero_grad() actor_loss.backward(retain_graph=True) self.actor_nn.optimizer.step() self.critic_local_1_nn.optimizer.zero_grad() self.critic_local_2_nn.optimizer.zero_grad() q_hat = Hyper.reward_scale * reward + Hyper.gamma * new_value_from_nn action_logits1 = self.critic_local_1_nn(state) q1_old_policy = T.argmax(action_logits1, dim=1, keepdim=True).view(-1) action_logits2 = self.critic_local_2_nn(state) q2_old_policy = T.argmax(action_logits2, dim=1, keepdim=True).view(-1) critic_1_loss = 0.5 * F.mse_loss(q1_old_policy, q_hat) critic_2_loss = 0.5 * F.mse_loss(q2_old_policy, q_hat) critic_loss = critic_1_loss + critic_2_loss critic_loss.backward() self.critic_local_1_nn.optimizer.step() self.critic_local_2_nn.optimizer.step() self.update_network_parameters() def update_network_parameters(self, tau=None): if tau is None: tau = Hyper.tau target_value_params = self.target_value_nn.named_parameters() value_params = self.value_nn.named_parameters() target_value_state_dict = dict(target_value_params) value_state_dict = dict(value_params) for name in value_state_dict: value_state_dict[name] = tau*value_state_dict[name].clone() + \ (1-tau)*target_value_state_dict[name].clone() self.target_value_nn.load_state_dict(value_state_dict) self.update_network_parameters_line( self.critic_target_1_nn.named_parameters(), self.critic_local_1_nn.named_parameters(), tau) self.update_network_parameters_line( self.critic_target_2_nn.named_parameters(), self.critic_local_2_nn.named_parameters(), tau) def update_network_parameters_line(self, target_params, local_params, tau): for target_param, local_param in zip(target_params, local_params): target_param[1].data.copy_(tau * local_param[1].data + (1.0 - tau) * target_param[1].data) def save_models(self): print('.... saving models ....') self.actor_nn.save_checkpoint() self.value_nn.save_checkpoint() self.target_value_nn.save_checkpoint() self.critic_local_1_nn.save_checkpoint() self.critic_local_2_nn.save_checkpoint() self.critic_target_1_nn.save_checkpoint() self.critic_target_2_nn.save_checkpoint() def load_models(self): print('.... loading models ....') self.actor_nn.load_checkpoint() self.value_nn.load_checkpoint() self.target_value_nn.load_checkpoint() self.critic_local_1_nn.load_checkpoint() self.critic_local_2_nn.load_checkpoint() self.critic_target_1_nn.load_checkpoint() self.critic_target_2_nn.load_checkpoint()
class Agent(): def __init__(self, alpha=0.0003, beta= 0.0003, input_dims=[8], env=None, gamma=0.99, n_actions=2, max_size=1000000, tau=0.005, ent_alpha = 0.0001, batch_size=256, reward_scale=2, layer1_size=256, layer2_size=256, chkpt_dir='tmp/sac'): self.gamma = gamma self.tau = tau self.memory = ReplayBuffer(max_size, input_dims, n_actions) self.batch_size = batch_size self.n_actions = n_actions self.ent_alpha = ent_alpha self.reward_scale = reward_scale self.actor = ActorNetwork(alpha, input_dims, n_actions=n_actions, fc1_dims=layer1_size, fc2_dims=layer2_size , name='actor', chkpt_dir=chkpt_dir) self.critic_1 = CriticNetwork(beta, input_dims, n_actions=n_actions, fc1_dims=layer1_size, fc2_dims=layer2_size ,name='critic_1', chkpt_dir=chkpt_dir) self.critic_2 = CriticNetwork(beta, input_dims, n_actions=n_actions, fc1_dims=layer1_size, fc2_dims=layer2_size ,name='critic_2', chkpt_dir=chkpt_dir) self.target_critic_1 = CriticNetwork(beta, input_dims, n_actions=n_actions, fc1_dims=layer1_size, fc2_dims=layer2_size ,name='target_critic_1', chkpt_dir=chkpt_dir) self.target_critic_2 = CriticNetwork(beta, input_dims, n_actions=n_actions, fc1_dims=layer1_size, fc2_dims=layer2_size ,name='target_critic_2', chkpt_dir=chkpt_dir) self.update_network_parameters(tau=1) def choose_actions(self, observation, learn_mode=False): if not learn_mode: state = T.Tensor([observation]).to(self.actor.device) else: state = observation action_probs = self.actor.forward(state) max_probability_action = T.argmax(action_probs, dim=-1) action_distribution = Categorical(action_probs) action = action_distribution.sample().cpu().detach().numpy()[0] z = action_probs == 0.0 z = z.float()*1e-8 log_probs = T.log(action_probs + z) return action, action_probs, log_probs, max_probability_action def remember(self, state, action, reward, new_state, done): self.memory.store_transition(state, action, reward, new_state, done) def update_network_parameters(self, tau=None): if tau is None: tau = self.tau target_critic_1_params = self.target_critic_1.named_parameters() target_critic_2_params = self.target_critic_2.named_parameters() critic_1_params = self.critic_1.named_parameters() critic_2_params = self.critic_2.named_parameters() target_critic_1_state_dict = dict(target_critic_1_params) critic_1_state_dict = dict(critic_1_params) target_critic_2_state_dict = dict(target_critic_2_params) critic_2_state_dict = dict(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() self.target_critic_1.load_state_dict(critic_1_state_dict) self.target_critic_2.load_state_dict(critic_2_state_dict) def save_models(self): # print('.....saving models.....') self.actor.save_checkpoint() self.critic_1.save_checkpoint() self.critic_2.save_checkpoint() self.target_critic_1.save_checkpoint() self.target_critic_2.save_checkpoint() def load_models(self): print('.....loading models.....') self.actor.load_checkpoint() self.critic_1.load_checkpoint() self.critic_2.load_checkpoint() self.target_critic_1.load_checkpoint() self.target_critic_2.load_checkpoint() def learn(self): if self.memory.mem_cntr < self.batch_size: return 10, 10, 10, 10, 10 state, action, reward, next_state, done = self.memory.sample_buffer(self.batch_size) reward = T.tensor(reward, dtype=T.float).to(self.actor.device) done = T.tensor(done, dtype=T.float).to(self.actor.device) state_ = T.tensor(next_state, dtype=T.float).to(self.actor.device) state = T.tensor(state, dtype=T.float).to(self.actor.device) action = T.tensor(action, dtype=T.float).to(self.actor.device) # Critics Learning with T.no_grad(): action_, probs, log_probs, max_action = self.choose_actions(state_, learn_mode=True) qf1_target_ = self.target_critic_1(state_) qf2_target_ = self.target_critic_2(state_) min_qf_target_ = probs*(T.min(qf1_target_, qf2_target_) - self.ent_alpha*log_probs) min_qf_target_ = min_qf_target_.sum(dim=1).view(-1) next_q_value = reward + (1.0-done)*self.gamma*min_qf_target_ action = action.view(self.batch_size, 1) qf1 = self.critic_1(state).gather(1, action.long()) qf2 = self.critic_2(state).gather(1, action.long()) self.critic_1.optimizer.zero_grad() qf1_loss = F.mse_loss(qf1, next_q_value) qf1_loss.backward(retain_graph=False) self.critic_1.optimizer.step() self.critic_2.optimizer.zero_grad() qf2_loss = F.mse_loss(qf2, next_q_value) qf2_loss.backward(retain_graph=False) self.critic_2.optimizer.step() self.update_network_parameters() # Actor loss qf1_pi = self.critic_1(state) qf2_pi = self.critic_2(state) min_qf_pi = T.min(qf1_pi, qf2_pi) inside_term = self.ent_alpha*log_probs - min_qf_pi actor_loss = (probs*inside_term).sum(dim=1).mean() self.actor.optimizer.zero_grad() actor_loss.backward(retain_graph=False) self.actor.optimizer.step() def compute_grads(self): if self.memory.mem_cntr < self.batch_size: return False state, action, reward, next_state, done = self.memory.sample_buffer(self.batch_size) reward = T.tensor(reward, dtype=T.float).to(self.actor.device) done = T.tensor(done, dtype=T.float).to(self.actor.device) state_ = T.tensor(next_state, dtype=T.float).to(self.actor.device) action = T.tensor(action, dtype=T.float).to(self.actor.device) state = T.tensor(state, dtype=T.float).to(self.actor.device) state.requires_grad = True with T.no_grad(): action_, probs, log_probs, max_action = self.choose_actions(state_, learn_mode=True) qf1_target_ = self.target_critic_1(state_) qf2_target_ = self.target_critic_2(state_) min_qf_target_ = probs*(T.min(qf1_target_, qf2_target_) - self.ent_alpha*log_probs) min_qf_target_ = min_qf_target_.sum(dim=1).view(-1) next_q_value = reward + (1.0-done)*self.gamma*min_qf_target_ self.actor.optimizer.zero_grad() qf1_pi = self.critic_1(state) qf2_pi = self.critic_2(state) min_qf_pi = T.min(qf1_pi, qf2_pi) inside_term = self.ent_alpha*log_probs - min_qf_pi actor_loss = (probs*inside_term).sum(dim=1).mean() actor_loss.backward() data_grad = state.grad.data return data_grad.mean(axis=0)