class DqnAgent():
"""Deep Q-Network agent that interacts with and learns from the environment."""
def __init__(self, id, state_size, action_size, seed, use_double=False, use_prio=False, use_dueling=False):
"""Initialize an Agent object.
Params
======
id (int): id used to identify the agent
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
double (boolean): Use Double DQN algorithm
use_prio (boolean): Use Prioritized Experience Replay
use_dueling (boolean): Use Dueling DQN algorithm
"""
self.state_size = state_size
self.action_size = action_size
self.id = id
self.use_double = use_double
self.use_prio = use_prio
self.use_dueling = use_dueling
self.seed = random.seed(seed)
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Q-Network
if use_dueling:
self.qnetwork_local = DuelingQNetwork(state_size, action_size, seed).to(self.device)
self.qnetwork_target = DuelingQNetwork(state_size, action_size, seed).to(self.device)
else:
self.qnetwork_local = QNetwork(state_size, action_size, seed).to(self.device)
self.qnetwork_target = QNetwork(state_size, action_size, seed).to(self.device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
if use_prio:
self.memory = NaivePrioritizedReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed, PRIO_ALPHA, PRIO_EPSILON)
else:
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, beta=PRIO_BETA):
# 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:
if self.use_prio:
experiences, weights = self.memory.sample(beta)
states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(self.device)
actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(self.device)
rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(self.device)
next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(self.device)
dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(self.device)
weights = torch.from_numpy(np.vstack(weights)).float().to(self.device)
experiences = (states, actions, rewards, next_states, dones)
self.learn(experiences, GAMMA, weights)
else:
experiences = self.memory.sample()
states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(self.device)
actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(self.device)
rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(self.device)
next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(self.device)
dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(self.device)
experiences = (states, actions, rewards, next_states, dones)
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(self.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()).item()
else:
return random.choice(np.arange(self.action_size)).item()
def learn(self, experiences, gamma, weights=None):
"""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
weights (array_like): list of weights for compensation the non-uniform sampling (used only
with prioritized experience replay)
"""
states, actions, rewards, next_states, dones = experiences
if self.use_double:
# Evaluate the greedy policy according to the local network
target_next_indices_local = self.qnetwork_local(next_states).detach().max(1)[1].unsqueeze(1)
# Get max predicted Q values (for next states) from target model
Q_targets_next = self.qnetwork_target(next_states).detach().gather(1, target_next_indices_local)
else:
# 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
if self.use_prio:
td_error = Q_expected - Q_targets
loss = (td_error) ** 2
loss = loss * weights
loss = loss.mean()
self.memory.update_priorities(np.hstack(td_error.detach().cpu().numpy()))
else:
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 getId(self):
""" Return the ID of the agent """
return self.id
def summary(self):
""" Return a brief summary of the agent"""
s = 'DQN Agent {}: Double: {}, PER: {}, Dueling: {}\n'.format(self.id, self.use_double, self.use_prio, self.use_dueling)
s += self.qnetwork_local.__str__()
s += '\nMemory size: {} \nBatch size: {}\nGamma: {}\nLR: {}\nTau: {}\nUpdate every: {}'.format(BUFFER_SIZE, BATCH_SIZE, GAMMA, LR, TAU, UPDATE_EVERY)
return s
