def _optimize(self):
"""Sample batch from experience replay pool and update the policy"""
if len(self.memory) < self.BATCH_SIZE:
return
transitions, weights, idxes = self.memory.sample(
self.BATCH_SIZE, self.beta.anneal())
batch = Transition(*zip(*transitions))
states = torch.cat(batch.state)
actions = torch.cat(batch.action)
rewards = torch.cat(batch.reward)
weights = torch.tensor(weights,
dtype=torch.float32,
device=self.device)
q = self._q(states, actions)
expected_q = self._expected_q(batch.next_state, rewards)
# update the priority of each transition
td_error = expected_q - q
new_priorities = torch.abs(td_error) + self.eps
self.memory.update_priorities(idxes, new_priorities.flatten())
# Compute Huber loss
loss = F.smooth_l1_loss(q, expected_q, reduction='none')
loss = (weights * loss).mean()
# Optimize the model
self.optimizer.zero_grad()
loss.backward()
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
def optimize():
if len(replaymemory) < BATCH_SIZE:
return
# sample tuples
trainsitions = replaymemory.sample(BATCH_SIZE)
batch = Transition(*zip(*trainsitions))
actions = tuple((map(lambda a:torch.tensor([[a]], device=device), batch.action)))
rewards = tuple((map(lambda r:torch.tensor([r], device=device), batch.reward)))
state_batch = torch.cat(batch.state).to(device)
action_batch = torch.cat(actions)
reward_batch = torch.cat(rewards)
next_state_batch = batch.next_state
non_final_mask = torch.tensor(tuple(map(lambda s: s is not None, next_state_batch)),dtype=torch.bool, device=device)
non_final_next_states = torch.cat([s for s in next_state_batch if s is not None]).to(device)
#policy net output q value for cur state
state_action_values = policy_net(state_batch).gather(1, action_batch)
# target net output q value for cur state
next_state_values = torch.zeros(BATCH_SIZE, device=device)
next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach()
expected_state_action_values = (next_state_values * GAMMA) + reward_batch
loss = F.smooth_l1_loss(state_action_values, expected_state_action_values)
optimizer.zero_grad()
loss.backward()
for param in policy_net.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
pass
def train(self, ds):
dataset = ds.get_dataset()
loss = 0
q_score = 0
act = 0
N = 10000
l, s, n = 0, 0, 0
for i in range(30_000_000):
state, action, reward, next_state, raw = next(dataset)
self.agent.push(Transition(state, action, reward, next_state), i)
if i % 2 == 0 and i:
l, s = self.agent.train(i)
if l:
loss += l
q_score += s
n += 1
if i % N == 0:
act = self.agent.act(state, i)
#print(time.time() - self.t)
if n:
print(
'step %d, train: %d, act:%.2f, score:%.2f, loss:%.4f' %
(i, n, act, q_score / n, loss / n))
self.t = time.time()
loss, q_score, n = 0, 0, 0
act = 0.0
#print('update model file')
self.save()
def train(self, env: gym.Env, n_steps):
rewards = []
steps = []
episode_rewards = []
state = np_to_unsq_tensor(env.reset())
loop_range = tqdm.tqdm(range(n_steps))
for step in loop_range:
with torch.no_grad():
z = self.z_net(state)
if random.random() < self.epsilon: # Random action
action = torch.LongTensor([[env.action_space.sample()]])
else:
action = select_argmax_action(z, self.atoms)
next_state, reward, done, info = env.step(squeeze_np(action))
next_state = np_to_unsq_tensor(next_state) if not done else None
self.replay_buffer.remember(
Transition(state, action, torch.tensor([[reward]]),
next_state))
state = next_state
# Perform training step
self._train_step(step)
# Update episode stats
episode_rewards.append(reward)
if done:
state = np_to_unsq_tensor(env.reset())
rewards.append(sum(episode_rewards))
steps.append(step)
episode_rewards = []
loop_range.set_description(f'Reward {rewards[-1]}')
return Plot(steps, rewards, None)
def update(self):
if len(self.memory) < self.batch_size:
return
transitions = self.memory.sample(self.batch_size)
batch = Transition(*zip(*transitions))
# Compute Q(s', a') for all a'
# TODO: Use a target network???
next_qvals = self.network(batch.next_state, batch.next_acts)
# Take the max over next q-values
next_qvals = torch.tensor([vals.max() for vals in next_qvals],
device=device)
# Zero all the next_qvals that are done
next_qvals = next_qvals * (
1 - torch.tensor(batch.done, dtype=torch.float, device=device))
targets = torch.tensor(batch.reward, dtype=torch.float,
device=device) + self.gamma * next_qvals
# Next compute Q(s, a)
# Nest each action in a list - so that it becomes the only admissible cmd
nested_acts = tuple([[a] for a in batch.act])
qvals = self.network(batch.state, nested_acts)
# Combine the qvals: Maybe just do a greedy max for generality
qvals = torch.cat(qvals)
# Compute Huber loss
loss = F.smooth_l1_loss(qvals, targets.detach())
self.optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(self.network.parameters(), self.clip)
self.optimizer.step()
return loss.item()
def fit_buffer(self):
transitions = self.memory.sample(self.batch_size)
batch = Transition(*zip(*transitions))
# Update actor and critic according to the batch
actor_loss, critic_loss = self.agent.update_params(batch)
self.metrics['actor_loss'].append(actor_loss)
self.metrics['critic_loss'].append(critic_loss)
def update(self):
if len(self.memory) < self.BATCH_SIZE:
return
# get training batch
transitions = self.memory.sample(self.BATCH_SIZE)
batch = Transition(*zip(*transitions))
state_batch = torch.cat(batch.state)
action_batch = torch.cat(batch.action)
reward_batch = torch.cat(batch.reward).unsqueeze(1)
next_state = torch.cat(batch.next_state)
# update value network
state_action = torch.cat((state_batch, action_batch), dim=1)
state_action_value = self.value_network(state_action)
next_action = self.action_target_network(next_state).detach()
next_state_action = torch.cat((next_state, next_action), dim=1)
next_state_action_value = self.value_target_network(
next_state_action).detach()
expected_state_action_value = (self.DISCOUNT *
next_state_action_value) + reward_batch
value_loss = self.criterion(state_action_value,
expected_state_action_value)
self.value_optimizer.zero_grad()
value_loss.backward()
self.value_optimizer.step()
# update action network
optim_action = self.action_network(state_batch)
optim_state_action = torch.cat((state_batch, optim_action), dim=1)
action_loss = -self.value_network(optim_state_action)
action_loss = action_loss.mean()
self.action_optimizer.zero_grad()
action_loss.backward()
self.action_optimizer.step()
# update target network
soft_update(self.value_target_network, self.value_network, 0.01)
soft_update(self.action_target_network, self.action_network, 0.01)
def optimize(self, step):
# print(len(self.memory))
if len(self.memory) < self.batch_size * 10:
return
transitions = self.memory.sample(self.batch_size)
# Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for
# detailed explanation). This converts batch-array of Transitions
# to Transition of batch-arrays.
batch = Transition(*zip(*transitions))
# Compute a mask of non-final states and concatenate the batch elements
# (a final state would've been the one after which simulation ended)
next_state = torch.FloatTensor(batch.next_state).to(device)
non_final_mask = torch.tensor(tuple(map(lambda s: s is not None, next_state)))
non_final_next_states = torch.cat([s for s in next_state if s is not None])
state_batch = torch.FloatTensor(batch.state).to(device)
action_batch = torch.LongTensor(torch.add(torch.tensor(batch.action), torch.tensor(1))).to(device)
reward_batch = torch.FloatTensor(batch.reward).to(device)
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the
# columns of actions taken. These are the actions which would've been taken
# for each batch state according to policy_net
l = self.policy_net(state_batch).size(0)
state_action_values = self.policy_net(state_batch)[95:l:96].gather(1, action_batch.reshape((self.batch_size, 1)))
state_action_values = state_action_values.squeeze(-1)
# Compute V(s_{t+1}) for all next states.
# Expected values of actions for non_final_next_states are computed based
# on the "older" target_net; selecting their best reward with max(1)[0].
# This is merged based on the mask, such that we'll have either the expected
# state value or 0 in case the state was final.
next_state_values = torch.zeros(self.batch_size, device=device)
next_state_values[non_final_mask] = self.target_net(next_state)[95:l:96].max(1)[0].detach()
# Compute the expected Q values
expected_state_action_values = (next_state_values * self.gamma) + reward_batch
# Compute the loss
loss = torch.nn.MSELoss()(expected_state_action_values, state_action_values)
# Optimize the model
loss.backward()
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
if step % self.T == 0:
# print('soft_update')
gamma = 0.001
param_before = copy.deepcopy(self.target_net)
target_update = copy.deepcopy(self.target_net.state_dict())
for k in target_update.keys():
target_update[k] = self.target_net.state_dict()[k] * (1 - gamma) + self.policy_net.state_dict()[k] * gamma
self.target_net.load_state_dict(target_update)
def update(self):
if len(self.memory) < self.batch_size:
return
batch_loss = None
num_per_step = int(self.batch_size / self.accummulate_step)
for _ in range(self.accummulate_step):
transitions = self.memory.sample(num_per_step)
batch = Transition(*zip(*transitions))
# Compute Q(s', a') for all a'
# TODO: Use a target network???
next_history = []
for act, history in zip(batch.act, batch.history):
next_history.append(history + [act])
next_qvals = self.network(batch.next_state, batch.next_acts,
next_history)
# Take the max over next q-values
next_qvals = torch.tensor([vals.max() for vals in next_qvals],
device=device)
# Zero all the next_qvals that are done
next_qvals = next_qvals * (
1 - torch.tensor(batch.done, dtype=torch.float, device=device))
targets = torch.tensor(batch.reward,
dtype=torch.float,
device=device) + self.gamma * next_qvals
# Next compute Q(s, a)
# Nest each action in a list - so that it becomes the only admissible cmd
nested_acts = tuple([[a] for a in batch.act])
qvals = self.network(batch.state, nested_acts, batch.history)
# Combine the qvals: Maybe just do a greedy max for generality
qvals = torch.cat(qvals)
loss = F.smooth_l1_loss(qvals, targets.detach())
# Compute Huber loss
if batch_loss is None:
batch_loss = loss
else:
batch_loss += loss
batch_loss /= num_per_step
self.optimizer.zero_grad()
batch_loss.backward()
nn.utils.clip_grad_norm_(self.network.parameters(), self.clip)
self.optimizer.step()
# self.scheduler.step()
return loss.item()
def optimize_model(self, config):
#transitions = self.memory.sample(config.batch_size)
# PrioritizedReplayMemory
transitions, weights, indices = self.memory.sample(
config.batch_size, config.beta)
transitions = self.transition_to_tensor(transitions)
batch = Transition(*zip(*transitions))
loss, weights_loss = self.get_loss(batch, config, weights,
config.gamma)
# N Step
transitions_n, _, _ = self.memory_n.sample_from_indices(
config.batch_size, config.beta, indices)
transitions_n = self.transition_to_tensor(transitions_n)
batch_n = Transition(*zip(*transitions_n))
gamma_n = config.gamma**config.n_step
loss_n, weights_loss_n = self.get_loss(batch_n, config, weights,
gamma_n)
weights_loss += weights_loss_n
self.optimizer.zero_grad()
#loss.backward()
# PrioritizedReplayMemory
weights_loss.backward()
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
# PrioritizedReplayMemory
loss_for_prior = loss.detach().cpu().numpy()
new_priorities = loss_for_prior + config.prior_eps
self.memory.update_priorities(indices, new_priorities)
# N Step
self.memory_n.update_priorities(indices, new_priorities)
# Noisy Net
self.policy_net.reset_noise()
self.target_net.reset_noise()
def optimize_model():
if len(memory) < BATCH_SIZE:
return
transitions = memory.sample(BATCH_SIZE)
# Transpose the batch (see http://stackoverflow.com/a/19343/3343043 for
# detailed explanation). This converts batch-array of Transitions
# to Transition of batch-arrays.
batch = Transition(*zip(*transitions))
# Compute a mask of non-final states and concatenate the batch elements
# (a final state would've been the one after which simulation ended)
non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,
batch.next_state)), device=device, dtype=torch.uint8)
non_final_next_states = torch.cat([s for s in batch.next_state
if s is not None])
state_batch = torch.cat(batch.state)
action_batch = torch.cat(batch.action)
reward_batch = torch.cat(batch.reward)
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the
# columns of actions taken. These are the actions which would've been taken
# for each batch state according to policy_net
state_action_values = policy_net(state_batch).gather(1, action_batch)
# Compute V(s_{t+1}) for all next states.
# Expected values of actions for non_final_next_states are computed based
# on the "older" target_net; selecting their best reward with max(1)[0].
# This is merged based on the mask, such that we'll have either the expected
# state value or 0 in case the state was final.
next_state_values = torch.zeros(BATCH_SIZE, device=device)
next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach()
# Compute the expected Q values
expected_state_action_values = (next_state_values * GAMMA) + reward_batch
# Compute Huber loss
loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))
# Optimize the model
optimizer.zero_grad()
loss.backward()
for param in policy_net.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
def optimize():
if len(memory) < BATCH_SIZE:
return
transitions = memory.sample(BATCH_SIZE)
batch = Transition(*zip(*transitions))
states = torch.cat(batch.state)
actions = torch.cat(batch.action)
rewards = torch.cat(batch.reward)
actual_q = policy_net(states).gather(1, actions)
expected_q_value = expected_q(batch.next_state, rewards)
loss = F.smooth_l1_loss(
actual_q, expected_q_value) # loss between actual q and expected q
# optimize the model
optimizer.zero_grad()
loss.backward()
for param in policy_net.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
def step(self):
self.env.steps_done += 1
x = self.env.input().reshape(self.n_agents, -1).cuda(self.device)
phi = []
for i in range(self.n_agents):
with torch.no_grad():
phi.append(self.Encoder(x[i]))
# after encoding
n = torch.cat(phi, dim=0).reshape(self.n_agents, -1)
# state
s = []
for i in range(self.n_agents):
ni = torch.cat((n[0:i], n[i + 1:])).reshape(-1)
s.append(torch.cat((x[i], ni)))
s = torch.cat(s).reshape(self.n_agents, -1)
# epsilon-greedy
actions = self.select_action(s)
# collect rewards
rewards = self.env.step(actions)
if rewards == -1:
return "done"
# state_{t+1}
x_tp1 = self.env.input().reshape(self.n_agents, -1).cuda(self.device)
phi_tp1 = []
for i in range(self.n_agents):
with torch.no_grad():
phi_tp1.append(self.Encoder(x_tp1[i]))
n_tp1 = torch.cat(phi_tp1, dim=0).reshape(self.n_agents, -1)
s_tp1 = []
for i in range(self.n_agents):
ni = torch.cat((n_tp1[0:i], n_tp1[i + 1:])).reshape(-1)
s_tp1.append(torch.cat((x_tp1[i], ni)))
s_tp1 = torch.cat(s_tp1).reshape(self.n_agents, -1)
# initial Transition tuple
res = []
for i in range(self.n_agents):
res.append(
Transition(state=s[i],
action=actions[i],
next_state=s_tp1[i],
reward=rewards[i]))
return res
def _optimize(self):
if len(self.memory) < self.BATCH_SIZE:
return
transitions = self.memory.sample(self.BATCH_SIZE)
batch = Transition(*zip(*transitions))
states = torch.cat(batch.state)
actions = torch.cat(batch.action)
rewards = torch.cat(batch.reward)
# calculate q value and expected q value
q = self._q(states, actions)
expected_q = self._expected_q(batch.next_state, rewards)
loss = F.smooth_l1_loss(q, expected_q)
# optimize the model
self.optimizer.zero_grad()
loss.backward()
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
def learn(self, mem):
transitions = mem.sample(self.batch_size)
batch = Transition(*zip(*transitions)) # Transpose the batch
states = Variable(torch.stack(batch.state, 0))
actions = Variable(torch.LongTensor(batch.action).unsqueeze(1))
rewards = Variable(torch.Tensor(batch.reward))
non_final_mask = torch.ByteTensor(
tuple(map(
lambda s: s is not None,
batch.next_state))) # Only process non-terminal next states
next_states = Variable(
torch.stack(tuple(s for s in batch.next_state if s is not None),
0),
volatile=True
) # Prevent backpropagating through expected action values
Qs = self.policy_net(states).gather(1, actions) # Q(s_t, a_t; θpolicy)
next_state_argmax_indices = self.policy_net(next_states).max(
1, keepdim=True
)[1] # Perform argmax action selection using policy network: argmax_a[Q(s_t+1, a; θpolicy)]
Qns = Variable(torch.zeros(
self.batch_size)) # Q(s_t+1, a) = 0 if s_t+1 is terminal
Qns[non_final_mask] = self.target_net(next_states).gather(
1, next_state_argmax_indices
) # Q(s_t+1, argmax_a[Q(s_t+1, a; θpolicy)]; θtarget)
Qns.volatile = False # Remove volatile flag to prevent propagating it through loss
target = rewards + (
self.discount * Qns
) # Double-Q target: Y = r + γ.Q(s_t+1, argmax_a[Q(s_t+1, a; θpolicy)]; θtarget)
loss = F.smooth_l1_loss(
Qs, target) # Huber loss on TD-error δ: δ = Y - Q(s_t, a_t)
# TODO: TD-error clipping?
self.policy_net.zero_grad()
loss.backward()
nn.utils.clip_grad_norm(self.policy_net.parameters(),
self.max_gradient_norm) # Clamp gradients
self.optimiser.step()
def optimize_model(policy_net, optimizer):
# first sample a batch
if len(memory) < BATCH_SIZE:
return
transitions = memory.sample(BATCH_SIZE)
batch = Transition(*zip(*transitions))
# non_final_mask is the mask to tag all the item whose next_state is not None as True
non_final_mask = tuple(map(lambda s: s is not None, batch.next_state))
non_final_mask = torch.tensor(non_final_mask,
device=device,
dtype=torch.uint8)
non_final_next_states = torch.cat(
[s for s in batch.next_state if s is not None])
state_batch = torch.cat(batch.state)
action_batch = torch.cat(batch.action)
reward_batch = torch.cat(batch.reward)
# policy_net(state_batch) is used to get all value among all actions
# gather method is used to get the value corresponding to certain action
state_action_values = policy_net(state_batch).gather(1, action_batch)
next_state_values = torch.zeros(BATCH_SIZE, device=device)
# compute the V(s_{t+1}) for $s_{t+1}$ which is final state, we set V(s_{t+1}) = 0
next_state_values[non_final_mask] = target_net(non_final_next_states).max(
1)[0].detach()
expected_state_action_values = (next_state_values * GAMMA) + reward_batch
# Huber loss
loss = F.smooth_l1_loss(state_action_values,
expected_state_action_values.unsqueeze(1))
optimizer.zero_grad()
loss.backward()
for param in policy_net.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
def optimize(self, batch_size, global_step=None):
if len(self.memory) < batch_size:
return None
self.memory.batch_size = batch_size
for transitions_batch in self.memory:
# transform list of tuples into a tuple of lists.
# explanation here: https://stackoverflow.com/a/19343/3343043
batch = Transition(*zip(*transitions_batch))
state_batch = torch.cat(batch.state)
next_state_batch = torch.cat(batch.next_state)
action_batch = torch.cat(batch.action)
reward_batch = torch.cat(batch.reward)
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the
# columns of actions taken. These are the actions which would've been taken
# for each batch state according to policy_net
state_action_values = self.model(state_batch).gather(
1, action_batch.reshape(batch_size, 1))
# Compute the expected Q values
with torch.no_grad():
next_state_values = self.model(next_state_batch).max(
1)[0].detach()
expected_state_action_values = (next_state_values *
self.gamma) + reward_batch
# Compute Huber loss
loss = F.smooth_l1_loss(state_action_values,
expected_state_action_values.unsqueeze(1))
# Optimize the model
self.optimizer.zero_grad()
loss.backward()
self.writer.add_scalar('training/loss', loss.item(), global_step)
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=2)
self.optimizer.step()
def update(self):
if len(self.memory) < self.BATCH_SIZE:
print("[Warning] Memory data less than batch sizes!")
return
transitions = self.memory.sample(self.BATCH_SIZE)
batch = Transition(*zip(*transitions))
final_mask = torch.cat(batch.done)
non_final_next_states = torch.cat(
[s for s in batch.next_state if s is not None])
state_batch = torch.cat(batch.state)
action_batch = torch.cat(batch.action)
reward_batch = torch.cat(batch.reward)
state_action_values = self.policy_net(state_batch).gather(
1, action_batch)
next_state_values = torch.zeros(self.BATCH_SIZE, 1, device=device)
next_state_values[final_mask.bitwise_not()] = self.target_net(
non_final_next_states).max(1, True)[0].detach()
expected_state_action_values = (next_state_values *
self.GAMMA) + reward_batch
loss = F.smooth_l1_loss(state_action_values,
expected_state_action_values)
self.optimizer.zero_grad()
loss.backward()
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
self.update_count += 1
if self.update_count % self.TARGET_UPDATE == 0:
self.update_target_net()
def optimize_model(policy_net, target_net, optimizer, memory):
if len(memory) < BATCH_SIZE:
return
transitions = memory.sample(BATCH_SIZE)
batch = Transition(*zip(*transitions))
# compute a mask of non-final states
non_final_mask = torch.tensor([s is not None for s in batch.next_state],
device=device, dtype=torch.uint8)
non_final_next_states = torch.cat([s for s in batch.next_state if s is not None])
state_batch = torch.cat(batch.state)
action_batch = torch.cat(batch.action)
reward_batch = torch.cat(batch.reward)
# model computes Q(s_t)
# use this to compute Q(s_t, a)
state_action_values = policy_net(state_batch).gather(1, action_batch)
# V(s_{t+1})
# all final states have 0 value
# double Q-learning implemented
policy_best_actions = policy_net(non_final_next_states).argmax(dim=1)
i = torch.arange(len(policy_best_actions))
next_state_values = torch.zeros(BATCH_SIZE, device=device)
next_state_values[non_final_mask] = target_net(non_final_next_states)[i, policy_best_actions].detach()
# expected Q values
expected_state_action_values = reward_batch + (next_state_values * GAMMA)
loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(dim=1))
optimizer.zero_grad()
loss.backward()
for param in policy_net.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
def optimize(self):
transitions = self.memory.sample(500)
normalized_transitions = Transition(*zip(*transitions))
def optimize_model(
dqn_net,
target_net,
memory,
learning_rate,
batch_size,
size_board,
gamma,
optimizer,
device,
):
# Sample batch
tree_indexes, memory_batch, batch_ISWeights = memory.sample(batch_size)
samples = Transition(*zip(*memory_batch))
states_batch = samples.state
actions_batch = samples.action
rewards_batch = samples.reward
next_states_batch = samples.next_state
dones_batch = samples.done
target_qs_batch = []
torch_next_states_batch = (torch.from_numpy(
np.asarray(next_states_batch)).float().to(device))
# Get Q values for next state
q_next_state = dqn_net(torch_next_states_batch, batch_size, size_board)
# REMOVER detach depois e testar !!!!!!!!!!!
q_target_next_state = (target_net(torch_next_states_batch, batch_size,
size_board).cpu().detach())
for i in range(0, len(memory_batch)):
terminal = dones_batch[i]
# Get max action value index
action = np.argmax(q_next_state[i].cpu().detach().numpy())
# If we are in terminal state, only equals reward
if terminal:
target_qs_batch.append(rewards_batch[i])
else:
target = rewards_batch[i] + gamma * q_target_next_state[i][action]
target_qs_batch.append(target)
targets_batch = np.array([each for each in target_qs_batch])
torch_states_batch = torch.from_numpy(
np.asarray(states_batch)).float().to(device)
output = dqn_net(torch_states_batch, batch_size, size_board)
torch_actions_batch = torch.from_numpy(np.asarray(actions_batch))
torch_actions_batch = torch_actions_batch.unsqueeze(0)
torch_actions_batch = torch_actions_batch.view(batch_size, 1)
# Q is our predicted Q value
q_values = output.gather(1, torch_actions_batch.to(device))
q_values = q_values.float()
# Absolute error for update tree
absolute_errors = (
torch.abs(q_values - torch.from_numpy(targets_batch).view(
batch_size, 1).float().to(device)).cpu().detach().numpy())
torch_batch_ISWeights = torch.from_numpy(batch_ISWeights).to(device)
# Mean squared error
diff_target = q_values - torch.from_numpy(targets_batch).view(
batch_size, 1).float().to(device)
squared_diff = diff_target**2
weighted_squared_diff = squared_diff * torch_batch_ISWeights
# Loss
loss = torch.mean(weighted_squared_diff)
# Optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Squeze absolute errors
absolute_errors = np.squeeze(absolute_errors, 1)
# Memory tree update
memory.batch_update(tree_indexes, absolute_errors)
return loss.cpu().detach().numpy()
def optimize_model(self):
if (len(self.memory) < self.batch_size
or self.train_step % self.update_every != 0):
return
transitions = self.memory.sample(self.batch_size)
# transpose the batch so that we get a transition
# of batch array
batch = Transition(*zip(*transitions))
# mask for non-final states
# these are states where we will have another move to make
# after the current move
non_final_mask = torch.logical_not(
torch.tensor(batch.done, dtype=torch.bool, device=self.device))
# get the next states that are not final
non_final_next_state_batch = torch.tensor(
[s['obs'] for s in batch.next_state],
dtype=torch.float,
device=self.device)[non_final_mask]
# get the legal actions for these non-final
# next states
non_final_next_state_legal_actions = torch.stack([
torch.tensor(self.pad_actions(s['legal_actions']),
dtype=torch.long,
device=self.device) for s in batch.next_state
],
dim=0)[non_final_mask]
# get the state, action, and reward batches
state_batch = torch.tensor([s['obs'] for s in batch.state],
dtype=torch.float,
device=self.device)
action_batch = torch.tensor(batch.action,
dtype=torch.long,
device=self.device).view(-1, 1)
reward_batch = torch.tensor(batch.reward,
dtype=torch.float,
device=self.device)
# compute Q(s_t, a)
state_action_values = self.policy_net(state_batch).gather(
1, action_batch)
# compute max a for Q(s_{t+1}, a)
# if s_{t+1} is a final state, then Q(s_{t+1}, a) is 0
next_state_values = torch.zeros(self.batch_size, device=self.device)
if non_final_next_state_batch.size()[0] != 0:
# get predicted rewards for all non-final next states
next_state_all_values = \
self.target_net(non_final_next_state_batch).detach()
# only select rewards for valid actions
next_state_valid_values = next_state_all_values.gather(
1, non_final_next_state_legal_actions)
# get the max reward for a valid action
next_state_values[non_final_mask] = (
next_state_valid_values.max(1)[0])
expected_state_action_values = ((next_state_values * self.gamma) +
reward_batch)
# minimize Q(s_t, a) - reward + (gamma * max a Q(s_{t+1}, a))
# the predicted total reward for choosing action a at state s_t
# should equal the actual reward for that action plus the
# predicted total reward for choosing another action at state s_{t+1}
loss = self.criterion(state_action_values,
expected_state_action_values.unsqueeze(1))
self.loss_sum += loss.item()
self.optimizer.zero_grad()
loss.backward()
self.weight_updates += 1
if self.clip_grads:
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
if self.weight_updates % self.target_update == 0:
self.target_net.load_state_dict(self.policy_net.state_dict())
def update(self, state, action, reward, state_next, done):
record = Transition(state, action, reward, state_next, done)
self._memory.add(record)
if len(self._memory) >= self._batch_size:
self.train_step()
mask = np.array([not done])
nextState = stateToTensor(nextState, desiredGoal)
nextStateNumpy = nextState.cpu().numpy()
reward = calcReward(state[0, -3:], desiredGoal, orginalDistance)
episodeReward = reward
shortMemory.push(state, action, mask, nextStateNumpy, reward)
if done:
break
else:
state = nextState.to(device)
if len(memory) > batchSize:
for _ in range(updatesPerStep):
transition = memory.sample(batchSize)
batch = Transition(*zip(*transition))
valueLoss = agent.updateParameters(batch, device)
valueLossEp += valueLoss
memory.append(shortMemory)
rewards.append(episodeReward)
if episode % checkEvery == 0:
testRewards = []
for _ in range(numberOfTests):
state = env.reset()
startingPositionPuck = state["achieved_goal"]
orginalDistance = np.linalg.norm(startingPositionPuck -
desiredGoal)
while True:
state = stateToTensor(state, desiredGoal).to(device=device)
def compute_bellman_residual(self, batch, target_state_action_value=None):
# Compute concatenate the batch elements
if not isinstance(batch.current_state, torch.Tensor):
# logger.info("Casting the batch to torch.tensor")
current_state = torch.cat(
tuple(torch.tensor([batch.current_state],
dtype=torch.float))).to(self.device)
current_future_pos = torch.cat(
tuple(
torch.tensor([batch.current_future_pos],
dtype=torch.float))).to(self.device)
current_past_pos = torch.cat(
tuple(torch.tensor([batch.current_past_pos],
dtype=torch.float))).to(self.device)
action = torch.tensor(batch.action,
dtype=torch.long).to(self.device)
reward = torch.tensor(batch.reward,
dtype=torch.float).to(self.device)
next_state = torch.cat(
tuple(torch.tensor([batch.next_state],
dtype=torch.float))).to(self.device)
next_future_pos = torch.cat(
tuple(torch.tensor([batch.next_future_pos],
dtype=torch.float))).to(self.device)
next_past_pos = torch.cat(
tuple(torch.tensor([batch.next_past_pos],
dtype=torch.float))).to(self.device)
terminal = torch.tensor(batch.terminal,
dtype=torch.bool).to(self.device)
batch = Transition(current_state, current_future_pos, current_past_pos,\
action, reward,\
next_state, next_future_pos, next_past_pos,\
terminal, batch.info)
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the
# columns of actions taken
current_state_action_values, current_trajectory = self.value_net(
batch.current_state, batch.current_past_pos, batch.action)
state_action_values = current_state_action_values.gather(
1, batch.action.unsqueeze(1)).squeeze(1)
if target_state_action_value is None:
with torch.no_grad():
# Compute V(s_{t+1}) for all next states.
next_state_values = torch.zeros(batch.reward.shape).to(
self.device)
if self.config["double"]:
# Double Q-learning: pick best actions from policy network
next_state_action_values, next_trajectory = self.value_net(
batch.next_state, batch.next_past_pos)
_, best_actions = next_state_action_values.max(1)
# Double Q-learning: estimate action values from target network
next_target_state_action_values, next_target_tragectory = self.target_net(
batch.next_state, batch.next_past_pos)
best_values = next_target_state_action_values.gather(
1, best_actions.unsqueeze(1)).squeeze(1)
else:
next_state_action_values, next_trajectory = self.target_net(
batch.next_state, bacth.next_past_pos)
best_values, _ = next_state_action_values.max(1)
next_state_values[~batch.terminal] = best_values[~batch.
terminal]
# Compute the expected Q values
target_state_action_value = batch.reward + self.config[
"gamma"] * next_state_values
# Compute loss
rl_loss = self.rl_lossFunction(state_action_values,
target_state_action_value)
predict_loss = self.predict_lossfunction(current_trajectory,
batch.current_future_pos)
self.writer.add_scalar('step/rl_loss', rl_loss, self.step)
self.writer.add_scalar('step/predict_loss', predict_loss, self.step)
return rl_loss + predict_loss, target_state_action_value, batch
def sample_minibatch(self):
if len(self.memory) < self.config["batch_size"]:
return None
transitions = self.memory.sample(self.config["batch_size"])
return Transition(*zip(*transitions))
duration.append(lifespan[i][-1])
lifespan[i].append(0)
if lifespan[i][-1] > 0: # 500일때 버림
memory.push(s[i], a[i], r[i], s_gotten[i], done[i])
if frame_count > initial_exploration:
eps -= 0.00005
eps = max(eps, 0.1)
batch = memory.sample(batch_size)
s = torch.FloatTensor([*batch.s]).to(device)
a = torch.LongTensor([*batch.a]).unsqueeze(-1).to(device)
r = torch.FloatTensor([*batch.r]).unsqueeze(-1).to(device)
ns = torch.FloatTensor([*batch.ns]).to(device)
nt = torch.BoolTensor(np.array(batch.nt).tolist()).unsqueeze(-1).to(device)
agent.train_model(Transition(s, a, r, ns, nt), solver, gamma, F.mse_loss)
if frame_count % update_target == 0:
agent.update()
if len(duration) > 100:
score = np.array(duration)[-100:].mean()
print('score:', score)
if score > 498:
break
frame_count += 1
envs.close()
env = gym.make(env_name)
s = env.reset()
while True:
preprocessed_s = torch.FloatTensor(s).unsqueeze(0).to(device)
a = agent.response(preprocessed_s)
def train_dqn(settings):
required_settings = [
"batch_size",
"checkpoint_frequency",
"device",
"eps_start",
"eps_end",
"eps_cliff",
"eps_decay",
"gamma",
"log_freq",
"logs_dir",
"lr",
"max_steps",
"memory_size",
"model_name",
"num_episodes",
"out_dir",
"target_net_update_freq",
]
if not settings_is_valid(settings, required_settings):
raise Exception(
f"Settings object {settings} missing some required settings.")
batch_size = settings["batch_size"]
checkpoint_frequency = settings["checkpoint_frequency"]
device = settings["device"]
eps_start = settings["eps_start"]
eps_end = settings["eps_end"]
eps_cliff = settings["eps_cliff"]
# eps_decay = settings["eps_decay"]
gamma = settings["gamma"]
logs_dir = settings["logs_dir"]
log_freq = settings["log_freq"]
lr = settings["lr"]
max_steps = settings["max_steps"]
memory_size = settings["memory_size"]
model_name = settings["model_name"]
num_episodes = settings["num_episodes"]
out_dir = settings["out_dir"]
target_net_update_freq = settings["target_net_update_freq"]
# Initialize environment
env = gym.make("StarGunner-v0")
# Initialize model
num_actions = env.action_space.n
settings["num_actions"] = num_actions
policy_net = DQN(settings).to(device)
target_net = DQN(settings).to(device)
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
# Initialize memory
logging.info("Initializing memory.")
memory = ReplayMemory(memory_size)
memory.init_with_random((1, 3, 84, 84), num_actions)
logging.info("Finished initializing memory.")
# Initialize other model ingredients
optimizer = optim.Adam(policy_net.parameters(), lr=lr)
# Initialize tensorboard
writer = SummaryWriter(logs_dir)
# Loop over episodes
policy_net.train()
steps_done = 0
log_reward_acc = 0.0
log_steps_acc = 0
for episode in tqdm(range(num_episodes)):
state = process_state(env.reset()).to(device)
reward_acc = 0.0
loss_acc = 0.0
# Loop over steps in episode
for t in range(max_steps):
with torch.no_grad():
Q = policy_net.forward(state.type(torch.float))
# Get best predicted action and perform it
if steps_done < eps_cliff:
epsilon = -(eps_start -
eps_end) / eps_cliff * steps_done + eps_start
else:
epsilon = eps_end
if random.random() < epsilon:
predicted_action = torch.tensor([env.action_space.sample()
]).to(device)
else:
predicted_action = torch.argmax(Q, dim=1)
next_state, raw_reward, done, info = env.step(
predicted_action.item())
# Note that next state could also be a difference
next_state = process_state(next_state)
reward = torch.tensor([clamp_reward(raw_reward)])
# Save to memory
memory.push(state.to("cpu"), predicted_action.to("cpu"),
next_state, reward)
# Move to next state
state = next_state.to(device)
# Sample from memory
batch = Transition(*zip(*memory.sample(batch_size)))
# Mask terminal state (adapted from https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html)
final_mask = torch.tensor(
tuple(map(lambda s: s is not None, batch.next_state)),
device=device,
dtype=torch.bool,
)
# print("FINAL_MASK", final_mask.shape)
state_batch = torch.cat(batch.state).type(torch.float).to(device)
next_state_batch = torch.cat(batch.next_state).type(
torch.float).to(device)
action_batch = torch.cat(batch.action).to(device)
reward_batch = torch.cat(batch.reward).to(device)
# print("STATE_BATCH SHAPE", state_batch.shape)
# print("STATE_BATCH", state_batch[4, :, 100])
# print("ACTION_BATCH SHAPE", action_batch.shape)
# print("ACTION_BATCH", action_batch)
# print("REWARD_BATCH SHAPE", reward_batch.shape)
# Compute Q
# Q_next = torch.zeros((batch_size, num_actions))
# print("MODEL STATE BATCH SHAPE", model(state_batch).shape)
Q_actual = policy_net(state_batch).gather(
1, action_batch.view(action_batch.shape[0], 1))
Q_next_pred = target_net(next_state_batch)
Q_max = torch.max(Q_next_pred, dim=1)[0].detach()
# print("Q_MAX shape", Q_max.shape)
target = reward_batch + gamma * Q_max * final_mask.to(Q_max.dtype)
# print("TARGET SIZE", target.shape)
# Calculate loss
loss = F.smooth_l1_loss(Q_actual, target.unsqueeze(1))
optimizer.zero_grad()
loss.backward()
# Clamp gradient to avoid gradient explosion
for param in policy_net.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
# Store stats
loss_acc += loss.item()
reward_acc += raw_reward
steps_done += 1
if steps_done % target_net_update_freq == 0:
target_net.load_state_dict(policy_net.state_dict())
# Exit if in terminal state
if done:
logging.debug(
f"Episode {episode} finished after {t} timesteps with reward {reward_acc}."
)
break
logging.debug(f"Loss: {loss_acc / t}")
# Save model checkpoint
if (episode != 0) and (episode % checkpoint_frequency == 0):
save_model_checkpoint(
policy_net,
optimizer,
episode,
loss,
f"{out_dir}/checkpoints/{model_name}_{episode}",
)
# Log to tensorboard
log_reward_acc += reward_acc
log_steps_acc += t
writer.add_scalar("Loss / Timestep", loss_acc / t, episode)
if episode % log_freq == 0:
writer.add_scalar("Reward", log_reward_acc / log_freq, episode)
writer.add_scalar("Reward / Timestep",
log_reward_acc / log_steps_acc, episode)
writer.add_scalar("Duration", log_steps_acc / log_freq, episode)
writer.add_scalar("Steps", log_reward_acc / log_steps_acc,
steps_done)
log_reward_acc = 0.0
log_steps_acc = 0
# Save model
save_model(policy_net, f"{out_dir}/{model_name}.model")
# Report final stats
logging.info(f"Steps Done: {steps_done}")
env.close()
return policy_net