class DQN_Agent:
"""
PyTorch Implementation of DQN/DDQN.
"""
def __init__(self, state_size, action_size, args):
"""
Initialize a D4PG Agent.
"""
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.action_size = action_size
self.state_size = state_size
self.framework = args.framework
self.eval = args.eval
self.agent_count = 1
self.learn_rate = args.learn_rate
self.batch_size = args.batch_size
self.buffer_size = args.buffer_size
self.C = args.C
self._epsilon = args.epsilon
self.epsilon_decay = args.epsilon_decay
self.epsilon_min = args.epsilon_min
self.gamma = 0.99
self.rollout = args.rollout
self.tau = args.tau
self.momentum = 1
self.l2_decay = 0.0001
self.update_type = "hard"
self.t_step = 0
self.episode = 0
self.seed = 0
# Set up memory buffers
if args.prioritized_experience_replay:
self.memory = PERBuffer(args.buffersize, self.batchsize,
self.framestack, self.device, args.alpha,
args.beta)
self.criterion = WeightedLoss()
else:
self.memory = ReplayBuffer(self.device, self.buffer_size,
self.gamma, self.rollout)
# Initialize Q networks #
self.q = self._make_model(state_size, action_size, args.pixels)
self.q_target = self._make_model(state_size, action_size, args.pixels)
self._hard_update(self.q, self.q_target)
self.q_optimizer = self._set_optimizer(self.q.parameters(),
lr=self.learn_rate,
decay=self.l2_decay,
momentum=self.momentum)
self.new_episode()
@property
def epsilon(self):
"""
This property ensures that the annealing process is run every time that
E is called.
Anneals the epsilon rate down to a specified minimum to ensure there is
always some noisiness to the policy actions. Returns as a property.
"""
self._epsilon = max(self.epsilon_min, self.epsilon_decay**self.t_step)
return self._epsilon
def act(self, state, eval=False, pretrain=False):
"""
Select an action using epsilon-greedy π.
Always use greedy if not training.
"""
if np.random.random() > self.epsilon or not eval and not pretrain:
state = state.to(self.device)
with torch.no_grad():
action_values = self.q(state).detach().cpu()
action = action_values.argmax(dim=1).unsqueeze(0).numpy()
else:
action = np.random.randint(self.action_size, size=(1, 1))
return action.astype(np.long)
def step(self, state, action, reward, next_state, pretrain=False):
"""
Add the current SARS' tuple into the short term memory, then learn
"""
# Current SARS' stored in short term memory, then stacked for NStep
experience = (state, action, reward, next_state)
if self.rollout == 1:
self.memory.store_trajectory(state, torch.from_numpy(action),
torch.tensor([reward]), next_state)
else:
self.memory.store_experience(experience)
self.t_step += 1
# Learn after done pretraining
if not pretrain:
self.learn()
def learn(self):
"""
Trains the Deep QNetwork and returns action values.
Can use multiple frameworks.
"""
# Sample from replay buffer, REWARDS are sum of (ROLLOUT - 1) timesteps
# Already calculated before storing in the replay buffer.
# NEXT_STATES are ROLLOUT steps ahead of STATES
batch, is_weights, tree_idx = self.memory.sample(self.batch_size)
states, actions, rewards, next_states, terminal_mask = batch
q_values = torch.zeros(self.batch_size).to(self.device)
if self.framework == 'DQN':
# Max predicted Q values for the next states from the target model
q_values[terminal_mask] = self.q_target(next_states).detach().max(
dim=1)[0]
if self.framework == 'DDQN':
# Get maximizing ACTION under Q, evaluate actionvalue
# under q_target
# Max valued action under active network
max_actions = self.q(next_states).detach().argmax(1).unsqueeze(1)
# Use the active network action to get the value of the stable
# target network
q_values[terminal_mask] = self.q_target(
next_states).detach().gather(1, max_actions).squeeze(1)
targets = rewards + (self.gamma**self.rollout * q_values)
targets = targets.unsqueeze(1)
values = self.q(states).gather(1, actions)
#Huber Loss provides better results than MSE
if is_weights is None:
loss = F.smooth_l1_loss(values, targets)
#Compute Huber Loss manually to utilize is_weights with Prioritization
else:
loss, td_errors = self.criterion.huber(values, targets, is_weights)
self.memory.batch_update(tree_idx, td_errors)
# Perform gradient descent
self.q_optimizer.zero_grad()
loss.backward()
self.q_optimizer.step()
self._update_networks()
self.loss = loss.item()
def initialize_memory(self, pretrain_length, env):
"""
Fills up the ReplayBuffer memory with PRETRAIN_LENGTH number of experiences
before training begins.
"""
if len(self.memory) >= pretrain_length:
print("Memory already filled, length: {}".format(len(self.memory)))
return
print("Initializing memory buffer.")
while True:
done = False
env.reset()
state = env.state
while not done:
action = self.act(state, pretrain=True)
next_state, reward, done = env.step(action)
if done:
next_state = None
self.step(state, action, reward, next_state, pretrain=True)
states = next_state
if self.t_step % 50 == 0 or len(
self.memory) >= pretrain_length:
print("Taking pretrain step {}... memory filled: {}/{}\
".format(self.t_step, len(self.memory),
pretrain_length))
if len(self.memory) >= pretrain_length:
print("Done!")
self.t_step = 0
self._epsilon = 1
return
def new_episode(self):
"""
Handle any cleanup or steps to begin a new episode of training.
"""
self.memory.init_n_step()
self.episode += 1
def _update_networks(self):
"""
Updates the network using either DDPG-style soft updates (w/ param \
TAU), or using a DQN/D4PG style hard update every C timesteps.
"""
if self.update_type == "soft":
self._soft_update(self.q, self.q_target)
elif self.t_step % self.C == 0:
self._hard_update(self.q, self.q_target)
def _soft_update(self, active, target):
"""
Slowly updated the network using every-step partial network copies
modulated by parameter TAU.
"""
for t_param, param in zip(target.parameters(), active.parameters()):
t_param.data.copy_(self.tau * param.data +
(1 - self.tau) * t_param.data)
def _hard_update(self, active, target):
"""
Fully copy parameters from active network to target network. To be used
in conjunction with a parameter "C" that modulated how many timesteps
between these hard updates.
"""
target.load_state_dict(active.state_dict())
def _set_optimizer(self, params, lr, decay, momentum, optimizer="Adam"):
"""
Sets the optimizer based on command line choice. Defaults to Adam.
"""
if optimizer == "RMSprop":
return optim.RMSprop(params, lr=lr, momentum=momentum)
elif optimizer == "SGD":
return optim.SGD(params, lr=lr, momentum=momentum)
else:
return optim.Adam(params, lr=lr, weight_decay=decay)
def _make_model(self, state_size, action_size, use_cnn):
"""
Sets up the network model based on whether state data or pixel data is
provided.
"""
if use_cnn:
return QCNNetwork(state_size, action_size,
self.seed).to(self.device)
else:
return QNetwork(state_size, action_size, self.seed).to(self.device)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed, framework, buffer_type):
"""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.framework = framework
self.buffer_type = buffer_type
# 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
# def __init__(self, device, buffer_size, batch_size, alpha, beta):
if self.buffer_type == 'PER_ReplayBuffer':
self.memory = PER_ReplayBuffer(device, BUFFER_SIZE, BATCH_SIZE,
ALPHA, BETA)
if self.buffer_type == 'ReplayBuffer':
self.memory = ReplayBuffer(device, 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:
if self.buffer_type == 'ReplayBuffer':
experiences = self.memory.sample()
is_weights = None
idxs = None
if self.buffer_type == 'PER_ReplayBuffer':
experiences, is_weights, idxs = self.memory.sample()
self.criterion = WeightedLoss()
#print('debugging:', experiences )
self.learn(experiences, is_weights, idxs, 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() # use local network
# 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, is_weights, idxs, 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
if self.framework == 'DQN':
# 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
) #target network: which is uploaded slower than the local network
#print('Q_targets_next is ', Q_targets_next)
if self.framework == "DDQN":
#print("DDQN")
max_actions = self.qnetwork_local(next_states).detach().argmax(
1).unsqueeze(1)
#print('max_actions is ', max_actions)
#print('self.qnetwork_target(next_states).detach() is ',self.qnetwork_target(next_states).detach())
#Q_targets_next = self.qnetwork_target(next_states).detach().gather(1, max_actions).squeeze(1)
Q_targets_next = self.qnetwork_target(next_states).detach().gather(
1, max_actions)
#print('DDQN, Q', Q_targets_next)
#print('rewards is ', rewards)
# 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)
#------------------------------------------------------------------------------
#Huber Loss provides better results than MSE
if is_weights is None:
loss = F.smooth_l1_loss(Q_expected, Q_targets)
#Compute Huber Loss manually to utilize is_weights with Prioritization
else:
loss, td_errors = self.criterion.huber(Q_expected, Q_targets,
is_weights)
self.memory.batch_update(idxs, td_errors)
# Perform gradient descent
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)
