class DQNAgent():
"""Interacts with and learns from the environment."""
def __init__(self,
state_size,
action_size,
seed,
ddqn=False,
dueling=False,
priority=False):
"""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)
self.ddqn = ddqn
# 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 = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed,
priority)
# 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
experiences = self.memory.sample(get_n=UPDATE_EVERY)
self.update_error(experiences, GAMMA)
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
self.update_error(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.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
"*** YOUR CODE HERE ***"
if self.ddqn:
old_val = self.qnetwork_local(states).gather(-1, actions)
with torch.no_grad():
actions = self.qnetwork_local(next_states).argmax(-1,
keepdim=True)
maxQ = self.qnetwork_target(next_states).gather(-1, actions)
target = rewards + gamma * maxQ * (1 - dones)
else: # Normal DQN
with torch.no_grad():
maxQ = self.qnetwork_target(next_states).max(-1,
keepdim=True)[0]
target = rewards + gamma * maxQ * (1 - dones)
old_val = self.qnetwork_local(states).gather(-1, actions)
self.optimizer.zero_grad()
loss = self.qnetwork_local.criterion(old_val, target)
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def update_error(self, experiences, gamma):
states, actions, rewards, next_states, dones, idx = experiences
## TODO: compute and minimize the loss
"*** YOUR CODE HERE ***"
if self.ddqn:
old_val = self.qnetwork_local(states).gather(-1, actions)
with torch.no_grad():
actions = self.qnetwork_local(next_states).argmax(-1,
keepdim=True)
maxQ = self.qnetwork_target(next_states).gather(-1, actions)
target = rewards + gamma * maxQ * (1 - dones)
else: # Normal DQN
with torch.no_grad():
maxQ = self.qnetwork_target(next_states).max(-1,
keepdim=True)[0]
target = rewards + gamma * maxQ * (1 - dones)
old_val = self.qnetwork_local(states).gather(-1, actions)
error = torch.abs(old_val - target).detach().numpy().squeeze()
for i, err in zip(idx, error):
self.memory.error_buffer[i] = err
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)
class Agent():
def __init__(self, state_size, action_size, seed):
''' Initialization of the agent '''
# Initialize state / action space sizes, and the counter for updating
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.t_step = 0
# Initialize the two Q-networks (the local and target) and define the optimizer
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)
# Initialize agent's replay buffer
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
def step(self, state, action, reward, next_state, done):
''' Store experience and learn if it is time to do so '''
# 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 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 '''
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
# Set the Q-network to evaluation mode (turn off training layers such as dropouts) and do a forward pass for the state
# without computing gradients. Afterwards, return to training mode
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 soft_update(self, local_model, target_model, tau):
''' Soft update for the target Q-network '''
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 learn(self, experiences, gamma):
''' Update local network weights using sampled experience tuples (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples '''
# Unpack experiences
states, actions, rewards, next_states, dones = experiences
# Get predictions from local network, i.e. Q-values of the (state, action) pairs in the sampled batch of experiences
Q_expected = self.qnetwork_local(states).gather(1, actions)
# Get training targets, i.e. current reward + max predicted Q-value for next state by target network, for each (state, action)
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Now compute the loss wrt these new targets
loss = self.qnetwork_local.criterion(Q_expected, Q_targets)
self.optimizer.zero_grad() # ?!
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
