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 = 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)
# 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=0.0, training_mode=True):
"""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)
if training_mode is True:
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))
action = np.int32(action)
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
# 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)
class DQN(object):
def __init__(self,state_space,action_space,seed,update_every,batch_size,buffer_size,learning_rate):
self.action_space = action_space
self.state_space = state_space
self.seed = random.seed(seed)
self.batch_size = batch_size
self.buffer_size = buffer_size
self.learning_rate = learning_rate
self.update_every = update_every
self.qnetwork_local = QNetwork(state_space,action_space)
self.qnetwork_target = QNetwork(state_space,action_space)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),lr=learning_rate)
# Initialize replaybuffer
self.memory = ReplayBuffer(action_space,buffer_size,buffer_size,seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self,state,action,reward,next_state,done,GAMMA):
# Save the experience
self.memory.add_experience(state,action,reward,next_state,done)
# learn from the experience
self.t_step = (self.t_step + 1) % self.update_every
if self.t_step == 0:
if len(self.memory) > self.buffer_size:
experiences = self.memory.sample()
self.learn(experiences,GAMMA)
def act(self,state,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()
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.sample(np.arange(self.action_space))
def learn(self,experiences,GAMMA):
states,actions,rewards,next_states,dones = experiences
target_values = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
targets = reward + (GAMMA * target_values * (1-done))
action_values = self.qnetwork_local(states).gather(1,actions)
loss = F.mse_loss(action_values,targets)
loss.backward()
self.optimizer.step()
soft_update(TAU)
def soft_update(self,tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
"""
for local_param,target_param in zip(self.qnetwork_local.parameters(),self.qnetwork_target.parameters()):
local_param.data.copy_(tau*local_param.data + (1-tau)*target_param.data)
# self.qnetwork_local.parameters() = TAU*self.qnetwork_local.parameters() + (1-TAU)*self.qnetwork_target.parameters()
class DQN_Agent():
""" Interacts an learns from the environment. """
def __init__(self,
state_size,
action_size,
seed,
GAMMA=GAMMA,
TAU=TAU,
LR=LR,
UPDATE_EVERY=UPDATE_EVERY,
BUFFER_SIZE=BUFFER_SIZE,
BATCH_SIZE=BATCH_SIZE):
""" Initialize the agent.
==========
PARAMETERS
==========
state_size (int) = observation dimension of the environment
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.tau = TAU
self.lr = LR
self.update_every = UPDATE_EVERY
self.buffer_size = BUFFER_SIZE
self.batch_size = BATCH_SIZE
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
# instantiate online local and target network for weight updates
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=self.lr)
# create a replay buffer
self.memory = ReplayBuffer(action_size, self.buffer_size,
self.batch_size, seed, self.device)
# time steps for updating target network every time t_step % 4 == 0
self.t_step = 0
def step(self, state, action, reward, next_state, done):
''' Append a SARS sequence to memory, then every update_every steps learn from experiences'''
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % self.update_every
if self.t_step == 0:
# in case enough samples are available in internal memory, sample and learn
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self.learn(experiences, self.gamma)
def act(self, state, eps=0.):
""" Choose action from an epsilon-greedy policy
==========
PARAMETERS
==========
state (array) = current state space
eps (float) = epsilon, for epsilon-greedy action choice """
state = torch.from_numpy(state).float().unsqueeze(0).to(self.device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local.forward(state)
self.qnetwork_local.train()
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 the value parameters using experience tuples sampled from ReplayBuffer
==========
PARAMETERS
==========
experiences = Tuple of torch.Variable: SARS', done
gamma (float) = discount factor to weight rewards
"""
states, actions, rewards, next_states, dones = experiences
# calculate max predicted Q values for the next states using target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# calculate expected Q vaues from the local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# compute MSE Loss
loss = F.mse_loss(Q_expected, Q_targets)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.soft_update(self.qnetwork_local, self.qnetwork_target, self.tau)
def soft_update(self, local_model, target_model, tau):
""" Soft update for model parameters, every update steps as defined above
theta_target = tau * theta_local + (1-tau)*theta_target
==========
PARAMETERS
==========
local_model, target_model = PyTorch Models, weights will be copied from-to
tau = interpolation parameter, type=float
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(self.tau * local_param.data +
(1.0 - self.tau) * target_param.data)
