if step > UPDATE_START and step % UPDATE_INTERVAL == 0:
# Randomly sample a batch of transitions B = {(s, a, r, s', d)} from D
batch = random.sample(D, BATCH_SIZE)
batch = {
k: torch.cat([d[k] for d in batch], dim=0)
for k in batch[0].keys()
}
# Compute targets
y = batch['reward'] + DISCOUNT * (1 - batch['done']) * target_agent(
batch['next_state']).max(dim=1)[0]
# Update Q-function by one step of gradient descent
value_loss = (
agent(batch['state']).gather(1, batch['action']).squeeze(dim=1) -
y).pow(2).mean()
optimiser.zero_grad()
value_loss.backward()
optimiser.step()
if step > UPDATE_START and step % TARGET_UPDATE_INTERVAL == 0:
# Update target network
target_agent = create_target_network(agent)
if step > UPDATE_START and step % TEST_INTERVAL == 0:
agent.eval()
total_reward = test(agent)
pbar.set_description('Step: %i | Reward: %f' % (step, total_reward))
plot(step, total_reward, 'dqn')
agent.train()
class QAgent:
def __init__(self, epsilon_start, epsilon_end, epsilon_anneal, nb_actions,
learning_rate, gamma, batch_size, replay_memory_size,
hidden_size, model_input_size, use_PER, use_ICM):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.epsilon_start = epsilon_start
self.epsilon_end = epsilon_end
self.epsilon_anneal_over_steps = epsilon_anneal
self.num_actions = nb_actions
self.gamma = gamma
self.batch_size = batch_size
self.learning_rate = learning_rate
self.step_no = 0
self.policy = DQN(hidden_size=hidden_size,
inputs=model_input_size,
outputs=nb_actions).to(self.device)
self.target = DQN(hidden_size=hidden_size,
inputs=model_input_size,
outputs=nb_actions).to(self.device)
self.target.load_state_dict(self.policy.state_dict())
self.target.eval()
self.hidden_size = hidden_size
self.optimizer = torch.optim.AdamW(self.policy.parameters(),
lr=self.learning_rate)
self.use_PER = use_PER
if use_PER:
self.replay = Prioritized_Replay_Memory(replay_memory_size)
else:
self.replay = Replay_Memory(replay_memory_size)
self.loss_function = torch.nn.MSELoss()
self.use_ICM = use_ICM
if use_ICM:
self.icm = ICM(model_input_size, nb_actions)
# Get the current epsilon value according to the start/end and annealing values
def get_epsilon(self):
eps = self.epsilon_end
if self.step_no < self.epsilon_anneal_over_steps:
eps = self.epsilon_start - self.step_no * \
((self.epsilon_start - self.epsilon_end) /
self.epsilon_anneal_over_steps)
return eps
# select an action with epsilon greedy
def select_action(self, state):
self.step_no += 1
if np.random.uniform() > self.get_epsilon():
with torch.no_grad():
return torch.argmax(self.policy(state)).view(1)
else:
return torch.tensor([random.randrange(self.num_actions)],
device=self.device,
dtype=torch.long)
# update the model according to one step td targets
def update_model(self):
if self.use_PER:
batch_index, batch, ImportanceSamplingWeights = self.replay.sample(
self.batch_size)
else:
batch = self.replay.sample(self.batch_size)
batch_tuple = Transition(*zip(*batch))
state = torch.stack(batch_tuple.state)
action = torch.stack(batch_tuple.action)
reward = torch.stack(batch_tuple.reward)
next_state = torch.stack(batch_tuple.next_state)
done = torch.stack(batch_tuple.done)
self.optimizer.zero_grad()
if self.use_ICM:
self.icm.optimizer.zero_grad()
forward_loss = self.icm.get_forward_loss(state, action, next_state)
inverse_loss = self.icm.get_inverse_loss(state, action, next_state)
icm_loss = (1 - self.icm.beta) * inverse_loss.mean(
) + self.ICM.beta * forward_loss.mean()
td_estimates = self.policy(state).gather(1, action).squeeze()
td_targets = reward + (1 - done.float()) * self.gamma * \
self.target(next_state).max(1)[0].detach_()
if self.use_PER:
loss = (torch.tensor(ImportanceSamplingWeights, device=self.device)
* self.loss_function(td_estimates, td_targets)
).sum() * self.loss_function(td_estimates, td_targets)
errors = td_estimates - td_targets
self.replay.batch_update(batch_index, errors.data.numpy())
else:
loss = self.loss_function(td_estimates, td_targets)
if self.use_ICM:
loss = self.icm.lambda_weight * loss + icm_loss
loss.backward()
for param in self.policy.parameters():
param.grad.data.clamp_(-1, 1)
if self.use_ICM:
self.icm.optimizer.step()
self.optimizer.step()
return loss.item()
# set target net parameters to policy net parameters
def update_target(self):
self.target.load_state_dict(self.policy.state_dict())
# save model
def save(self, path, name):
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, os.path.join(path, name + ".pt"))
torch.save(self.policy.state_dict(), filename)
# load a model
def load(self, path):
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, path)
self.policy.load_state_dict(torch.load(filename))
# store experience in replay memory
def cache(self, state, action, reward, next_state, done):
self.replay.push(state, action, reward, next_state, done)
class DQNAgent():
"""Interacts with and learns from the environment."""
def __init__(self, name, state_size, action_size, use_double_dqn=False, use_dueling=False, seed=0, lr_decay=0.9999, use_prioritized_replay=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.name = name
self.state_size = state_size
self.action_size = action_size
self.use_double_dqn = use_double_dqn
self.use_dueling = use_dueling
self.seed = random.seed(seed)
self.use_prioritized_replay = use_prioritized_replay
# Q-Network
if use_dueling:
self.qnetwork_local = DuelingDQN(state_size, action_size, seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size, seed).to(device)
else:
self.qnetwork_local = DQN(state_size, action_size, seed).to(device)
self.qnetwork_target = DQN(state_size, action_size, seed).to(device)
self.qnetwork_target.eval()
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
self.lr_scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer, lr_decay)
# Replay memory
if self.use_prioritized_replay:
self.memory = PrioritizedReplayBuffer(BUFFER_SIZE, seed, alpha=0.2, beta=0.8, beta_scheduler=1.0)
else:
self.memory = ReplayBuffer(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):
# 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(BATCH_SIZE)
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(device)
# Epsilon-greedy action selection
if random.random() > eps:
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
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.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
if self.use_prioritized_replay:
states, actions, rewards, next_states, dones, indices, weights = experiences
else:
states, actions, rewards, next_states, dones = experiences
with torch.no_grad():
# Get max predicted Q values (for next states) from target model
if self.use_double_dqn:
best_local_actions = self.qnetwork_local(states).max(1)[1].unsqueeze(1)
Q_targets_next = self.qnetwork_target(next_states).gather(1, best_local_actions).max(1)[0].unsqueeze(1)
else:
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)
if self.use_prioritized_replay:
Q_targets.sub_(Q_expected)
Q_targets.squeeze_()
Q_targets.pow_(2)
with torch.no_grad():
td_error = Q_targets.detach()
#td_error.pow_(0.5)
td_error.mul_(weights)
self.memory.update_priorities(indices, td_error)
Q_targets.mul_(weights)
loss = Q_targets.mean()
else:
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.lr_scheduler.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)
import torch.optim as optim
import copy
import pickle
from utils import *
from models import DQN
initial_Q = AER_initial_Q()
# initial_Q = torch.zeros(n_actions, device=device)
policy_net = DQN(recent_k, n_agents, n_actions, initial_Q).to(device)
target_net = DQN(recent_k, n_agents, n_actions, initial_Q).to(device)
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
optimizer = optim.RMSprop(policy_net.parameters())
Q = torch.zeros(n_actions, n_actions, n_actions, device=device)
for i in range(n_actions):
for j in range(n_actions):
Q[i, j, :] = initial_Q.view(-1)
memory = ReplayMemory(MEM_SIZE)
heat = torch.zeros(n_agents, n_actions, n_actions, device=device)
heat_unique0 = []
heat_freq0 = []
heat_unique1 = []
heat_freq1 = []
class FixedDQNAgent(DQNAgent):
"""
DQN Agent with a target network to compute Q-targets.
Extends DQNAgent.
"""
def __init__(self,
input_dim,
output_dim,
lr,
gamma,
max_memory_size,
batch_size,
eps_start,
eps_end,
eps_decay,
device,
target_update=100,
linear1_units=64,
linear2_units=64,
decay_type="linear"):
super().__init__(input_dim, output_dim, lr, gamma, max_memory_size,
batch_size, eps_start, eps_end, eps_decay, device,
linear1_units, linear2_units, decay_type)
self.model_name = "FixedDQN"
self.target_update_freq = target_update
# networks
self.output_dim = output_dim
self.target_net = DQN(input_dim, output_dim, linear1_units,
linear2_units).to(device)
self.target_net.load_state_dict(self.policy_net.state_dict())
self.target_net.eval()
self.updated = 0
def learn(self):
"""
Update the weights of the network, using
target_net to compute Q-targets. Every self.target_update_freq
updates, clone the policy_net.
:return: the loss
"""
states, next_states, actions, rewards, dones = self.memory.sample(
self.batch_size)
curr_q_vals = self.policy_net(states).gather(1, actions)
next_q_vals = self.target_net(next_states).max(
1, keepdim=True)[0].detach()
target = (rewards + self.gamma * next_q_vals * (1 - dones)).to(
self.device)
loss = F.smooth_l1_loss(curr_q_vals, target)
self.optim.zero_grad()
loss.backward()
self.optim.step()
self.updated += 1
if self.updated % self.target_update_freq == 0:
self.target_hard_update()
return loss.item()
def target_hard_update(self):
""" Clone the policy net weights into the target net """
self.target_net.load_state_dict(self.policy_net.state_dict())
class DQNAgent(BaseAgent):
"""
Agent with a DQN network.
"""
def __init__(self,
input_dim,
output_dim,
lr,
gamma,
max_memory_size,
batch_size,
eps_start,
eps_end,
eps_decay,
device,
linear1_units=64,
linear2_units=64,
decay_type="linear"):
super().__init__(max_memory_size, batch_size, eps_start, eps_end,
eps_decay, device, decay_type)
self.model_name = "DQN"
self.output_dim = output_dim
self.policy_net = DQN(input_dim, output_dim, linear1_units,
linear2_units).to(device)
# optimizer
self.optim = optim.Adam(self.policy_net.parameters(), lr=lr)
self.gamma = gamma
def choose_action(self, state, testing=False):
"""
Choose an action to perform. Uses eps-greedy approach.
:param state: current state of the environment
:param testing: if True, always choose greedy action
:return: the action chosen
"""
self.curr_step += 1
if not testing and np.random.random() < self.curr_eps:
return np.random.randint(0, self.output_dim)
else:
# we're using the network for inference only, we don't want to track the gradients in this case
with torch.no_grad():
return self.policy_net(state).argmax().item()
def learn(self):
"""
Update the weights of the network.
:return: the loss
"""
states, next_states, actions, rewards, dones = self.memory.sample(
self.batch_size)
curr_q_vals = self.policy_net(states).gather(1, actions)
next_q_vals = self.policy_net(next_states).max(
1, keepdim=True)[0].detach()
target = (rewards + self.gamma * next_q_vals * (1 - dones)).to(
self.device)
loss = F.smooth_l1_loss(curr_q_vals, target)
self.optim.zero_grad()
loss.backward()
self.optim.step()
return loss.item()
def set_test(self):
""" Sets the network in evaluation mode """
self.policy_net.eval()
def set_train(self):
""" Sets the network in training mode """
self.policy_net.train()
def save(self, filename):
"""
Save the network weights.
:param filename: path
"""
self.policy_net.save(filename)
def load(self, filename):
"""
Load the network weights.
:param filename: path of the weight file
"""
self.policy_net.load(filename, self.device)
