class Agent(): """Interacts with and learns from the environment.""" def __init__(self, state_size, action_size, seed, mode="train"): """Initialize an Agent object. Params ====== state_size (int): dimension of each state action_size (int): dimension of each action seed (int): random seed mode (str): if eval, the agent will not learn and collect experiences """ self.state_size = state_size self.action_size = action_size self.seed = random.seed(seed) # Q-Network self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device) self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device) self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR) # Replay memory self.memory = PrioritizedBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed) # Initialize time step (for updating every UPDATE_EVERY steps) self.t_step = 0 # Caches the expected action value of the last act self.last_action_value = None self.set_mode(mode) def step(self, state, action, reward, next_state, done): if self.mode == "train": error = self.calculate_error_eval(state, action, reward, next_state, done) # Save experience in replay memory self.memory.add(state, action, reward, next_state, done, error) # Learn every UPDATE_EVERY time steps. self.t_step = (self.t_step + 1) % UPDATE_EVERY if self.t_step == 0: # If enough samples are available in memory, get random subset and learn if len(self.memory) > BATCH_SIZE: experiences = self.memory.sample() self.learn(experiences, GAMMA) def act(self, state, eps=0.): """Returns actions for given state as per current policy. Params ====== state (array_like): current state eps (float): epsilon, for epsilon-greedy action selection """ if self.mode == "eval": eps = 0 state = torch.from_numpy(state).float().unsqueeze(0).to(device) self.qnetwork_local.eval() with torch.no_grad(): action_values = self.qnetwork_local(state) self.qnetwork_local.train() # Epsilon-greedy action selection if random.random() > eps: action = np.argmax(action_values.cpu().data.numpy()) else: action = random.choice(np.arange(self.action_size)) self.last_action_value = action_values[0][action].item() return action def learn(self, experiences, gamma): """Update value parameters using given batch of experience tuples. Params ====== experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples gamma (float): discount factor """ states, actions, rewards, next_states, dones = experiences ## TODO: compute and minimize the loss with torch.no_grad(): local_max = self.qnetwork_local(next_states).detach().argmax( 1).unsqueeze(1) targets = self.qnetwork_target(next_states).detach().gather( 1, local_max) target_values = rewards + gamma * targets * (1 - dones) predicted_values = self.qnetwork_local(states).gather(1, actions) criterion = torch.nn.MSELoss() loss = criterion(predicted_values, target_values) self.optimizer.zero_grad() loss.backward() self.optimizer.step() # ------------------- update target network ------------------- # self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU) self.memory.update_errors((target_values - predicted_values).squeeze()) def soft_update(self, local_model, target_model, tau): """Soft update model parameters. θ_target = τ*θ_local + (1 - τ)*θ_target Params ====== local_model (PyTorch model): weights will be copied from target_model (PyTorch model): weights will be copied to tau (float): interpolation parameter """ for target_param, local_param in zip(target_model.parameters(), local_model.parameters()): target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data) def calculate_error_eval(self, state, action, reward, next_state, done): """Calculates the error for a given step.""" self.qnetwork_target.eval() next_state = torch.from_numpy(next_state).float().unsqueeze(0).to( device) with torch.no_grad(): target = self.qnetwork_target(next_state).max() target_value = reward + GAMMA * target * (1 - done) error = (target_value - self.last_action_value).item() self.qnetwork_target.train() return error def save(self, path=""): torch.save(self.qnetwork_local.state_dict(), path + "checkpoint_local.pth") torch.save(self.qnetwork_target.state_dict(), path + "checkpoint_target.pth") def load(self, path=""): self.qnetwork_local.load_state_dict( torch.load(path + "checkpoint_local.pth")) self.qnetwork_target.load_state_dict( torch.load(path + "checkpoint_target.pth")) def set_mode(self, mode): if mode not in {"train", "eval"}: raise ValueError("mode must be one of [train, eval]") self.mode = mode
class Agent(): """Interacts with and learns from the environment.""" def __init__(self, state_size, action_size, seed , local_filename = None, target_filename = None): """Initialize an Agent object. Params ====== state_size (int): dimension of each state action_size (int): dimension of each action seed (int): random seed """ self.state_size = state_size self.action_size = action_size self.seed = random.seed(seed) # Q-Network self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device) self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device) ## if filename is given load them if local_filename is not None: self.qnetwork_local.load_state_dict(torch.load(local_filename)) if target_filename is not None: self.qnetwork_target.load_state_dict(torch.load(target_filename)) self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR) # Replay memory self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed) # Initialize time step (for updating every UPDATE_EVERY steps) self.t_step = 0 def step(self, state, action, reward, next_state, done): # Save experience in replay memory self.memory.add(state, action, reward, next_state, done) # Learn every UPDATE_EVERY time steps. self.t_step = (self.t_step + 1) % UPDATE_EVERY if self.t_step == 0: # If enough samples are available in memory, get random subset and learn if len(self.memory) > BATCH_SIZE: experiences = self.memory.sample() self.learn(experiences, GAMMA) def act(self, state, eps=0.): """Returns actions for given state as per current policy. Params ====== state (array_like): current state eps (float): epsilon, for epsilon-greedy action selection """ state = torch.from_numpy(state).float().unsqueeze(0).to(device) self.qnetwork_local.eval() with torch.no_grad(): action_values = self.qnetwork_local(state) self.qnetwork_local.train() # Epsilon-greedy action selection if random.random() > eps: return np.argmax(action_values.cpu().data.numpy()) else: return random.choice(np.arange(self.action_size)) def learn(self, experiences, gamma): """Update value parameters using given batch of experience tuples. Params ====== experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples gamma (float): discount factor """ states, actions, rewards, next_states, dones = experiences # Get max predicted Q values (for next states) from target model Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1) # Compute Q targets for current states Q_targets = rewards + (gamma * Q_targets_next * (1 - dones)) # Get expected Q values from local model Q_expected = self.qnetwork_local(states).gather(1, actions) # Compute loss loss = F.mse_loss(Q_expected, Q_targets) # Minimize the loss self.optimizer.zero_grad() loss.backward() self.optimizer.step() # ------------------- update target network ------------------- # self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU) def soft_update(self, local_model, target_model, tau): """Soft update model parameters. θ_target = τ*θ_local + (1 - τ)*θ_target Params ====== local_model (PyTorch model): weights will be copied from target_model (PyTorch model): weights will be copied to tau (float): interpolation parameter """ for target_param, local_param in zip(target_model.parameters(), local_model.parameters()): target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data) def save(self, local_save_filename, target_save_filename): torch.save(self.qnetwork_local.state_dict(), local_save_filename) torch.save(self.qnetwork_target.state_dict(), target_save_filename)