class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""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 = DuelingDQN(state_size, action_size,
seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
self.priority_alpha = 0.0 #current best: 03
self.priority_beta_start = 0.4
self.priority_beta_frames = BUFFER_SIZE
# Replay memory
self.memory = PrioritizedReplayMemory(BUFFER_SIZE, self.priority_alpha,
self.priority_beta_start,
self.priority_beta_frames)
# 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.push((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 self.memory.storage_size() > BATCH_SIZE:
#print("storage == ", self.memory.storage_size())
experiences, idxes, weights = self.memory.sample(BATCH_SIZE)
self.learn(experiences, idxes, weights, 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, idxes, weights, 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 = zip(*experiences)
states = torch.from_numpy(
np.vstack([state for state in states
if state is not None])).float().to(device)
actions = torch.from_numpy(
np.vstack([action for action in actions
if action is not None])).long().to(device)
rewards = torch.from_numpy(
np.vstack([reward for reward in rewards
if reward is not None])).float().to(device)
next_states = torch.from_numpy(
np.vstack([
next_state for next_state in next_states
if next_state is not None
])).float().to(device)
dones = torch.from_numpy(
np.vstack([done for done in dones if done is not None
]).astype(np.uint8)).float().to(device)
# Get max predicted Q values (for next states) from target model
#print("state-action values:")
#print(self.qnetwork_target(next_states).detach())
#print(next_states)
next_target_Q = self.qnetwork_target.forward(next_states)
#print("next_target_Q == ", next_target_Q)
_, next_local_Q_index = torch.max(
self.qnetwork_local.forward(next_states), axis=1)
#Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
Q_targets_next = next_target_Q[range(next_target_Q.shape[0]),
next_local_Q_index]
Q_targets_next1 = Q_targets_next.reshape((len(Q_targets_next), 1))
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next1 * (1 - dones))
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
#print(Q_expected)
#print(Q_targets)
diff = Q_expected - Q_targets
#print(diff)
#diff = diff.mean()
#print(idxes)
#print(diff.detach().squeeze().abs().cpu().numpy().tolist())
#update the priority of the replay buffer
self.memory.update_priorities(
idxes,
diff.detach().squeeze().abs().cpu().numpy().tolist())
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets) * weights
loss = loss.mean()
# 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)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self,
state_size,
action_size,
seed,
gamma=GAMMA,
buffer_size=BUFFER_SIZE,
batch_size=BATCH_SIZE,
update_every=UPDATE_EVERY,
lr=LR,
tau=TAU
):
"""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.gamma = gamma
self.batch_size = batch_size
# Q-Network
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.model_local = DuelingDQN(state_size, action_size, seed).to(self.device)
self.model_target = DuelingDQN(state_size, action_size, seed).to(self.device)
self.optimizer = optim.Adam(self.model_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(
action_size=action_size,
buffer_size=BUFFER_SIZE,
batch_size=BATCH_SIZE,
seed=seed,
device=self.device
)
# 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) > self.batch_size:
experiences = self.memory.sample()
self.update(experiences)
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.FloatTensor(state).float().unsqueeze(0).to(self.device)
self.model_local.eval()
with torch.no_grad():
qvals = self.model_local.forward(state)
self.model_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
action = np.argmax(qvals.cpu().detach().numpy())
return action
else:
return random.choice(np.arange(self.action_size))
def update(self, batch):
"""Update value parameters using given batch of experience tuples.
Params
======
batch (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = batch
# Get expected Q values from local model
curr_Q = self.model_local.forward(states).gather(1, actions)
# curr_Q = curr_Q.squeeze(1)
# Get max predicted Q values (for next states) from target model
max_next_Q = self.model_target.forward(next_states).detach().max(1)[0].unsqueeze(1)
expected_Q = rewards + (self.gamma * max_next_Q * (1 - dones))
loss = F.mse_loss(curr_Q, expected_Q)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# update target model
self.update_target(self.model_local, self.model_target, TAU)
def update_target(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)
