class ParametricDQNTrainer(RLTrainer):
def __init__(
self,
parameters: ContinuousActionModelParameters,
state_normalization_parameters: Dict[int, NormalizationParameters],
action_normalization_parameters: Dict[int, NormalizationParameters],
use_gpu: bool = False,
additional_feature_types:
AdditionalFeatureTypes = DEFAULT_ADDITIONAL_FEATURE_TYPES,
gradient_handler=None,
use_all_avail_gpus: bool = False,
) -> None:
self.double_q_learning = parameters.rainbow.double_q_learning
self.warm_start_model_path = parameters.training.warm_start_model_path
self.minibatch_size = parameters.training.minibatch_size
self.state_normalization_parameters = state_normalization_parameters
self.action_normalization_parameters = action_normalization_parameters
self.num_state_features = get_num_output_features(
state_normalization_parameters)
self.num_action_features = get_num_output_features(
action_normalization_parameters)
self.num_features = self.num_state_features + self.num_action_features
# ensure state and action IDs have no intersection
overlapping_features = set(
state_normalization_parameters.keys()) & set(
action_normalization_parameters.keys())
assert len(overlapping_features) == 0, (
"There are some overlapping state and action features: " +
str(overlapping_features))
reward_network_layers = deepcopy(parameters.training.layers)
reward_network_layers[0] = self.num_features
reward_network_layers[-1] = 1
if parameters.rainbow.dueling_architecture:
parameters.training.layers[0] = self.num_state_features
parameters.training.layers[-1] = 1
elif parameters.training.factorization_parameters is None:
parameters.training.layers[0] = self.num_features
parameters.training.layers[-1] = 1
else:
parameters.training.factorization_parameters.state.layers[
0] = self.num_state_features
parameters.training.factorization_parameters.action.layers[
0] = self.num_action_features
RLTrainer.__init__(self, parameters, use_gpu, additional_feature_types,
gradient_handler)
self.q_network = self._get_model(
parameters.training, parameters.rainbow.dueling_architecture)
self.q_network_target = deepcopy(self.q_network)
self._set_optimizer(parameters.training.optimizer)
self.q_network_optimizer = self.optimizer_func(
self.q_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
self.reward_network = FullyConnectedNetwork(
reward_network_layers, parameters.training.activations)
self.reward_network_optimizer = self.optimizer_func(
self.reward_network.parameters(),
lr=parameters.training.learning_rate)
if self.use_gpu:
self.q_network.cuda()
self.q_network_target.cuda()
self.reward_network.cuda()
if use_all_avail_gpus:
self.q_network = torch.nn.DataParallel(self.q_network)
self.q_network_target = torch.nn.DataParallel(
self.q_network_target)
self.reward_network = torch.nn.DataParallel(
self.reward_network)
def _get_model(self, training_parameters, dueling_architecture=False):
if dueling_architecture:
return DuelingQNetwork(
training_parameters.layers,
training_parameters.activations,
action_dim=self.num_action_features,
)
elif training_parameters.factorization_parameters is None:
return FullyConnectedNetwork(
training_parameters.layers,
training_parameters.activations,
use_noisy_linear_layers=training_parameters.
use_noisy_linear_layers,
)
else:
return ParametricInnerProduct(
FullyConnectedNetwork(
training_parameters.factorization_parameters.state.layers,
training_parameters.factorization_parameters.state.
activations,
),
FullyConnectedNetwork(
training_parameters.factorization_parameters.action.layers,
training_parameters.factorization_parameters.action.
activations,
),
self.num_state_features,
self.num_action_features,
)
def calculate_q_values(self, state_pas_concats, pas_lens):
row_nums = np.arange(pas_lens.shape[0], dtype=np.int64)
row_idxs = np.repeat(row_nums, pas_lens.cpu().numpy())
col_idxs = arange_expand(pas_lens)
dense_idxs = torch.LongTensor(
(row_idxs, col_idxs)).type(self.dtypelong)
q_values = self.q_network(state_pas_concats).squeeze().detach()
dense_dim = [len(pas_lens), int(torch.max(pas_lens))]
# Add specific fingerprint to q-values so that after sparse -> dense we can
# subtract the fingerprint to identify the 0's added in sparse -> dense
q_values.add_(self.FINGERPRINT)
sparse_q = torch.sparse_coo_tensor(dense_idxs,
q_values,
size=dense_dim)
dense_q = sparse_q.to_dense()
dense_q.add_(self.FINGERPRINT * -1)
dense_q[dense_q == self.FINGERPRINT *
-1] = self.ACTION_NOT_POSSIBLE_VAL
return dense_q
def get_max_q_values(self, possible_next_actions_state_concat, pnas_lens,
double_q_learning):
"""
:param possible_next_actions_state_concat: Numpy array with shape
(sum(pnas_lens), state_dim + action_dim). Each row
contains a representation of a state + possible next action pair.
:param pnas_lens: Numpy array that describes number of
possible_actions per item in minibatch
:param double_q_learning: bool to use double q-learning
"""
row_nums = np.arange(len(pnas_lens))
row_idxs = np.repeat(row_nums, pnas_lens.cpu().numpy())
col_idxs = arange_expand(pnas_lens).cpu().numpy()
dense_idxs = torch.LongTensor(
(row_idxs, col_idxs)).type(self.dtypelong)
if isinstance(possible_next_actions_state_concat, torch.Tensor):
q_network_input = possible_next_actions_state_concat
else:
q_network_input = torch.from_numpy(
possible_next_actions_state_concat).type(self.dtype)
if double_q_learning:
q_values = self.q_network(q_network_input).squeeze().detach()
q_values_target = self.q_network_target(
q_network_input).squeeze().detach()
else:
q_values = self.q_network_target(
q_network_input).squeeze().detach()
dense_dim = [len(pnas_lens), max(pnas_lens)]
# Add specific fingerprint to q-values so that after sparse -> dense we can
# subtract the fingerprint to identify the 0's added in sparse -> dense
q_values.add_(self.FINGERPRINT)
sparse_q = torch.sparse_coo_tensor(dense_idxs, q_values, dense_dim)
dense_q = sparse_q.to_dense()
dense_q.add_(self.FINGERPRINT * -1)
dense_q[dense_q == self.FINGERPRINT *
-1] = self.ACTION_NOT_POSSIBLE_VAL
max_q_values, max_indexes = torch.max(dense_q, dim=1)
if double_q_learning:
sparse_q_target = torch.sparse_coo_tensor(dense_idxs,
q_values_target,
dense_dim)
dense_q_values_target = sparse_q_target.to_dense()
max_q_values = torch.gather(dense_q_values_target, 1,
max_indexes.unsqueeze(1))
return max_q_values.squeeze()
def get_next_action_q_values(self, state_action_pairs):
return self.q_network_target(state_action_pairs)
def train(self,
training_samples: TrainingDataPage,
evaluator=None) -> None:
if self.minibatch == 0:
# Assume that the tensors are the right shape after the first minibatch
assert (training_samples.states.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.states.shape)
assert (training_samples.actions.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.actions.shape)
assert training_samples.rewards.shape == torch.Size(
[self.minibatch_size,
1]), "Invalid shape: " + str(training_samples.rewards.shape)
assert (training_samples.next_states.shape ==
training_samples.states.shape), "Invalid shape: " + str(
training_samples.next_states.shape)
assert (training_samples.not_terminals.shape ==
training_samples.rewards.shape), "Invalid shape: " + str(
training_samples.not_terminals.shape)
assert training_samples.possible_next_actions_state_concat.shape[
1] == (
training_samples.states.shape[1] +
training_samples.actions.shape[1]
), ("Invalid shape: " + str(
training_samples.possible_next_actions_state_concat.shape))
assert training_samples.possible_next_actions_lengths.shape == torch.Size(
[
self.minibatch_size
]), ("Invalid shape: " +
str(training_samples.possible_next_actions_lengths.shape))
self.minibatch += 1
states = training_samples.states.detach().requires_grad_(True)
actions = training_samples.actions
state_action_pairs = torch.cat((states, actions), dim=1)
rewards = training_samples.rewards
discount_tensor = torch.full(training_samples.time_diffs.shape,
self.gamma).type(self.dtype)
not_done_mask = training_samples.not_terminals
if self.use_seq_num_diff_as_time_diff:
discount_tensor = discount_tensor.pow(training_samples.time_diffs)
if self.maxq_learning:
# Compute max a' Q(s', a') over all possible actions using target network
next_q_values = self.get_max_q_values(
training_samples.possible_next_actions_state_concat,
training_samples.possible_next_actions_lengths,
self.double_q_learning,
)
else:
# SARSA
next_state_action_pairs = torch.cat(
(training_samples.next_states, training_samples.next_actions),
dim=1)
next_q_values = self.get_next_action_q_values(
next_state_action_pairs)
filtered_max_q_vals = next_q_values.reshape(-1, 1) * not_done_mask
if self.minibatch < self.reward_burnin:
target_q_values = rewards
else:
target_q_values = rewards + (discount_tensor * filtered_max_q_vals)
# Get Q-value of action taken
q_values = self.q_network(state_action_pairs)
all_action_scores = q_values.detach()
self.model_values_on_logged_actions = q_values.detach()
value_loss = self.q_network_loss(q_values, target_q_values)
self.loss = value_loss.detach()
self.q_network_optimizer.zero_grad()
value_loss.backward()
if self.gradient_handler:
self.gradient_handler(self.q_network.parameters())
self.q_network_optimizer.step()
if self.minibatch < self.reward_burnin:
# Reward burnin: force target network
self._soft_update(self.q_network, self.q_network_target, 1.0)
else:
# Use the soft update rule to update target network
self._soft_update(self.q_network, self.q_network_target, self.tau)
# get reward estimates
reward_estimates = self.reward_network(state_action_pairs)
reward_loss = F.mse_loss(reward_estimates, rewards)
self.reward_network_optimizer.zero_grad()
reward_loss.backward()
self.reward_network_optimizer.step()
self.loss_reporter.report(td_loss=float(self.loss),
reward_loss=float(reward_loss))
if evaluator is not None:
cpe_stats = BatchStatsForCPE(
model_values_on_logged_actions=all_action_scores)
evaluator.report(cpe_stats)
def predictor(self) -> ParametricDQNPredictor:
"""Builds a ParametricDQNPredictor."""
return ParametricDQNPredictor.export(
self,
self.state_normalization_parameters,
self.action_normalization_parameters,
self._additional_feature_types.int_features,
self.use_gpu,
)
def export(self) -> ParametricDQNPredictor:
return self.predictor()
class DQNTrainer(DQNTrainerBase):
def __init__(
self,
parameters: DiscreteActionModelParameters,
state_normalization_parameters: Dict[int, NormalizationParameters],
use_gpu: bool = False,
metrics_to_score=None,
gradient_handler=None,
use_all_avail_gpus: bool = False,
) -> None:
self.double_q_learning = parameters.rainbow.double_q_learning
self.bcq = parameters.rainbow.bcq
self.bcq_drop_threshold = parameters.rainbow.bcq_drop_threshold
self.warm_start_model_path = parameters.training.warm_start_model_path
self.minibatch_size = parameters.training.minibatch_size
self._actions = parameters.actions if parameters.actions is not None else []
if parameters.training.cnn_parameters is None:
self.state_normalization_parameters: Optional[Dict[
int, NormalizationParameters]] = state_normalization_parameters
self.num_features = get_num_output_features(
state_normalization_parameters)
logger.info("Number of state features: " + str(self.num_features))
parameters.training.layers[0] = self.num_features
else:
self.state_normalization_parameters = None
parameters.training.layers[-1] = self.num_actions
RLTrainer.__init__(
self,
parameters,
use_gpu,
metrics_to_score,
gradient_handler,
actions=self._actions,
)
self.reward_boosts = torch.zeros([1,
len(self._actions)]).type(self.dtype)
if parameters.rl.reward_boost is not None:
for k in parameters.rl.reward_boost.keys():
i = self._actions.index(k)
self.reward_boosts[0, i] = parameters.rl.reward_boost[k]
if parameters.rainbow.dueling_architecture:
self.q_network = DuelingQNetwork(
parameters.training.layers,
parameters.training.activations,
use_batch_norm=parameters.training.use_batch_norm,
)
else:
if parameters.training.cnn_parameters is None:
self.q_network = FullyConnectedNetwork(
parameters.training.layers,
parameters.training.activations,
use_noisy_linear_layers=parameters.training.
use_noisy_linear_layers,
min_std=parameters.training.weight_init_min_std,
use_batch_norm=parameters.training.use_batch_norm,
)
else:
self.q_network = ConvolutionalNetwork(
parameters.training.cnn_parameters,
parameters.training.layers,
parameters.training.activations,
use_noisy_linear_layers=parameters.training.
use_noisy_linear_layers,
min_std=parameters.training.weight_init_min_std,
use_batch_norm=parameters.training.use_batch_norm,
)
self.q_network_target = deepcopy(self.q_network)
self.q_network._name = "training"
self.q_network_target._name = "target"
self._set_optimizer(parameters.training.optimizer)
self.q_network_optimizer = self.optimizer_func(
self.q_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
self.clip_grad_norm = parameters.training.clip_grad_norm
self._init_cpe_networks(parameters, use_all_avail_gpus)
self._init_bcq_network(parameters, use_all_avail_gpus)
if self.use_gpu:
self.q_network.cuda()
self.q_network_target.cuda()
if use_all_avail_gpus:
self.q_network = torch.nn.DataParallel(self.q_network)
self.q_network_target = torch.nn.DataParallel(
self.q_network_target)
def _init_bcq_network(self, parameters, use_all_avail_gpus):
# Batch constrained q-learning
if not parameters.rainbow.bcq:
return
self.bcq_imitator = FullyConnectedNetwork(
parameters.training.layers,
parameters.training.activations,
min_std=parameters.training.weight_init_min_std,
use_batch_norm=parameters.training.use_batch_norm,
)
self.bcq_imitator_optimizer = self.optimizer_func(
self.bcq_imitator.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
if self.use_gpu:
self.bcq_imitator.cuda()
def _init_cpe_networks(self, parameters, use_all_avail_gpus):
if not self.calc_cpe_in_training:
return
reward_network_layers = deepcopy(parameters.training.layers)
if self.metrics_to_score:
num_output_nodes = len(self.metrics_to_score) * self.num_actions
else:
num_output_nodes = self.num_actions
reward_network_layers[-1] = num_output_nodes
self.reward_idx_offsets = torch.arange(
0, num_output_nodes, self.num_actions).type(self.dtypelong)
logger.info("Reward network for CPE will have {} output nodes.".format(
num_output_nodes))
if parameters.training.cnn_parameters is None:
self.reward_network = FullyConnectedNetwork(
reward_network_layers, parameters.training.activations)
self.q_network_cpe = FullyConnectedNetwork(
reward_network_layers, parameters.training.activations)
else:
self.reward_network = ConvolutionalNetwork(
parameters.training.cnn_parameters,
reward_network_layers,
parameters.training.activations,
)
self.q_network_cpe = ConvolutionalNetwork(
parameters.training.cnn_parameters,
reward_network_layers,
parameters.training.activations,
)
self.q_network_cpe_target = deepcopy(self.q_network_cpe)
self.q_network_cpe_optimizer = self.optimizer_func(
self.q_network_cpe.parameters(),
lr=parameters.training.learning_rate)
self.reward_network_optimizer = self.optimizer_func(
self.reward_network.parameters(),
lr=parameters.training.learning_rate)
if self.use_gpu:
self.reward_network.cuda()
self.q_network_cpe.cuda()
self.q_network_cpe_target.cuda()
if use_all_avail_gpus:
self.reward_network = torch.nn.DataParallel(
self.reward_network)
self.q_network_cpe = torch.nn.DataParallel(self.q_network_cpe)
self.q_network_cpe_target = torch.nn.DataParallel(
self.q_network_cpe_target)
@property
def num_actions(self) -> int:
return len(self._actions)
def calculate_q_values(self, states):
return self.q_network(states).detach()
def calculate_metric_q_values(self, states):
return self.q_network_cpe(states).detach()
def get_detached_q_values(self,
states) -> Tuple[torch.Tensor, torch.Tensor]:
with torch.no_grad():
q_values = self.q_network(states)
q_values_target = self.q_network_target(states)
return q_values, q_values_target
def get_next_action_q_values(self, states, next_actions):
"""
Used in SARSA update.
:param states: Numpy array with shape (batch_size, state_dim). Each row
contains a representation of a state.
:param next_actions: Numpy array with shape (batch_size, action_dim).
"""
q_values = self.q_network_target(states).detach()
# Max-q action indexes used in CPE
max_q_values, max_indicies = torch.max(q_values, dim=1, keepdim=True)
return (torch.sum(q_values * next_actions, dim=1,
keepdim=True), max_indicies)
def train(self, training_samples: TrainingDataPage):
if self.minibatch == 0:
# Assume that the tensors are the right shape after the first minibatch
assert (training_samples.states.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.states.shape)
assert training_samples.actions.shape == torch.Size([
self.minibatch_size, len(self._actions)
]), "Invalid shape: " + str(training_samples.actions.shape)
assert training_samples.rewards.shape == torch.Size(
[self.minibatch_size,
1]), "Invalid shape: " + str(training_samples.rewards.shape)
assert (training_samples.next_states.shape ==
training_samples.states.shape), "Invalid shape: " + str(
training_samples.next_states.shape)
assert (training_samples.not_terminal.shape ==
training_samples.rewards.shape), "Invalid shape: " + str(
training_samples.not_terminal.shape)
if training_samples.possible_next_actions_mask is not None:
assert (
training_samples.possible_next_actions_mask.shape ==
training_samples.actions.shape), (
"Invalid shape: " +
str(training_samples.possible_next_actions_mask.shape))
if training_samples.propensities is not None:
assert (training_samples.propensities.shape == training_samples
.rewards.shape), "Invalid shape: " + str(
training_samples.propensities.shape)
if training_samples.metrics is not None:
assert (
training_samples.metrics.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.metrics.shape)
boosted_rewards = self.boost_rewards(training_samples.rewards,
training_samples.actions)
self.minibatch += 1
states = training_samples.states.detach().requires_grad_(True)
actions = training_samples.actions
rewards = boosted_rewards
discount_tensor = torch.full(training_samples.time_diffs.shape,
self.gamma).type(self.dtype)
not_done_mask = training_samples.not_terminal
if self.use_seq_num_diff_as_time_diff:
time_diff = training_samples.time_diffs / self.time_diff_unit_length
discount_tensor = discount_tensor.pow(time_diff)
all_next_q_values, all_next_q_values_target = self.get_detached_q_values(
training_samples.next_states)
if self.bcq:
# Batch constrained q-learning
on_policy_actions = self.bcq_imitator(training_samples.next_states)
on_policy_action_probs = softmax(on_policy_actions, temperature=1)
filter_values = (
on_policy_action_probs /
on_policy_action_probs.max(keepdim=True, dim=1)[0])
action_on_policy = (filter_values >=
self.bcq_drop_threshold).float()
training_samples.possible_next_actions_mask *= action_on_policy
if self.maxq_learning:
# Compute max a' Q(s', a') over all possible actions using target network
next_q_values, max_q_action_idxs = self.get_max_q_values_with_target(
all_next_q_values,
all_next_q_values_target,
training_samples.possible_next_actions_mask,
)
else:
# SARSA
next_q_values, max_q_action_idxs = self.get_max_q_values_with_target(
all_next_q_values,
all_next_q_values_target,
training_samples.next_actions,
)
filtered_next_q_vals = next_q_values * not_done_mask
if self.minibatch < self.reward_burnin:
target_q_values = rewards
else:
target_q_values = rewards + (discount_tensor *
filtered_next_q_vals)
# Get Q-value of action taken
all_q_values = self.q_network(states)
self.all_action_scores = all_q_values.detach()
q_values = torch.sum(all_q_values * actions, 1, keepdim=True)
loss = self.q_network_loss(q_values, target_q_values)
self.loss = loss.detach()
self.q_network_optimizer.zero_grad()
loss.backward()
if self.gradient_handler:
self.gradient_handler(self.q_network.parameters())
if self.clip_grad_norm is not None:
torch.nn.utils.clip_grad_norm_(self.q_network.parameters(),
self.clip_grad_norm)
self.q_network_optimizer.step()
if self.minibatch < self.reward_burnin:
# Reward burnin: force target network
self._soft_update(self.q_network, self.q_network_target, 1.0)
else:
# Use the soft update rule to update target network
self._soft_update(self.q_network, self.q_network_target, self.tau)
bcq_loss = None
if self.bcq:
# Batch constrained q-learning
action_preds = self.bcq_imitator(states)
imitator_loss = torch.nn.CrossEntropyLoss()
# Classification label is index of action with value 1
bcq_loss = imitator_loss(action_preds,
torch.max(actions, dim=1)[1])
self.bcq_imitator_optimizer.zero_grad()
bcq_loss.backward()
self.bcq_imitator_optimizer.step()
logged_action_idxs = actions.argmax(dim=1, keepdim=True)
reward_loss, model_rewards, model_propensities = self.calculate_cpes(
training_samples,
states,
logged_action_idxs,
max_q_action_idxs,
discount_tensor,
not_done_mask,
)
self.loss_reporter.report(
td_loss=self.loss,
imitator_loss=bcq_loss,
reward_loss=reward_loss,
logged_actions=logged_action_idxs,
logged_propensities=training_samples.propensities,
logged_rewards=rewards,
logged_values=None, # Compute at end of each epoch for CPE
model_propensities=model_propensities,
model_rewards=model_rewards,
model_values=self.all_action_scores,
model_values_on_logged_actions=
None, # Compute at end of each epoch for CPE
model_action_idxs=self.get_max_q_values(
self.all_action_scores,
training_samples.possible_actions_mask)[1],
)
def calculate_cpes(
self,
training_samples,
states,
logged_action_idxs,
max_q_action_idxs,
discount_tensor,
not_done_mask,
):
if not self.calc_cpe_in_training:
return None, None, None
if training_samples.metrics is None:
metrics_reward_concat_real_vals = training_samples.rewards
else:
metrics_reward_concat_real_vals = torch.cat(
(training_samples.rewards, training_samples.metrics), dim=1)
######### Train separate reward network for CPE evaluation #############
reward_estimates = self.reward_network(states)
reward_estimates_for_logged_actions = reward_estimates.gather(
1, self.reward_idx_offsets + logged_action_idxs)
reward_loss = F.mse_loss(reward_estimates_for_logged_actions,
metrics_reward_concat_real_vals)
self.reward_network_optimizer.zero_grad()
reward_loss.backward()
self.reward_network_optimizer.step()
######### Train separate q-network for CPE evaluation #############
metric_q_values = self.q_network_cpe(states).gather(
1, self.reward_idx_offsets + logged_action_idxs)
metric_target_q_values = self.q_network_cpe_target(states).detach()
max_q_values_metrics = metric_target_q_values.gather(
1, self.reward_idx_offsets + max_q_action_idxs)
filtered_max_q_values_metrics = max_q_values_metrics * not_done_mask
if self.minibatch < self.reward_burnin:
target_metric_q_values = metrics_reward_concat_real_vals
else:
target_metric_q_values = metrics_reward_concat_real_vals + (
discount_tensor * filtered_max_q_values_metrics)
metric_q_value_loss = self.q_network_loss(metric_q_values,
target_metric_q_values)
self.q_network_cpe.zero_grad()
metric_q_value_loss.backward()
self.q_network_cpe_optimizer.step()
if self.minibatch < self.reward_burnin:
# Reward burnin: force target network
self._soft_update(self.q_network_cpe, self.q_network_cpe_target,
1.0)
else:
# Use the soft update rule to update target network
self._soft_update(self.q_network_cpe, self.q_network_cpe_target,
self.tau)
model_propensities = masked_softmax(
self.all_action_scores,
training_samples.possible_actions_mask,
self.rl_temperature,
)
model_rewards = reward_estimates[:,
torch.arange(
self.reward_idx_offsets[0],
self.reward_idx_offsets[0] +
self.num_actions,
), ]
return reward_loss, model_rewards, model_propensities
def boost_rewards(self, rewards: torch.Tensor,
actions: torch.Tensor) -> torch.Tensor:
# Apply reward boost if specified
reward_boosts = torch.sum(actions.float() * self.reward_boosts,
dim=1,
keepdim=True)
return rewards + reward_boosts
def predictor(self, set_missing_value_to_zero=False) -> DQNPredictor:
"""Builds a DQNPredictor."""
return DQNPredictor.export(
self,
self._actions,
self.state_normalization_parameters,
self.use_gpu,
set_missing_value_to_zero=set_missing_value_to_zero,
)
def export(self) -> DQNPredictor:
return self.predictor()
class ParametricDQNTrainer(DQNTrainerBase):
def __init__(
self,
parameters: ContinuousActionModelParameters,
state_normalization_parameters: Dict[int, NormalizationParameters],
action_normalization_parameters: Dict[int, NormalizationParameters],
use_gpu: bool = False,
additional_feature_types:
AdditionalFeatureTypes = DEFAULT_ADDITIONAL_FEATURE_TYPES,
metrics_to_score=None,
gradient_handler=None,
use_all_avail_gpus: bool = False,
) -> None:
self.double_q_learning = parameters.rainbow.double_q_learning
self.warm_start_model_path = parameters.training.warm_start_model_path
self.minibatch_size = parameters.training.minibatch_size
self.state_normalization_parameters = state_normalization_parameters
self.action_normalization_parameters = action_normalization_parameters
self.num_state_features = get_num_output_features(
state_normalization_parameters)
self.num_action_features = get_num_output_features(
action_normalization_parameters)
self.num_features = self.num_state_features + self.num_action_features
# ensure state and action IDs have no intersection
overlapping_features = set(
state_normalization_parameters.keys()) & set(
action_normalization_parameters.keys())
assert len(overlapping_features) == 0, (
"There are some overlapping state and action features: " +
str(overlapping_features))
reward_network_layers = deepcopy(parameters.training.layers)
reward_network_layers[0] = self.num_features
reward_network_layers[-1] = 1
if parameters.rainbow.dueling_architecture:
parameters.training.layers[0] = self.num_state_features
parameters.training.layers[-1] = 1
elif parameters.training.factorization_parameters is None:
parameters.training.layers[0] = self.num_features
parameters.training.layers[-1] = 1
else:
parameters.training.factorization_parameters.state.layers[
0] = self.num_state_features
parameters.training.factorization_parameters.action.layers[
0] = self.num_action_features
RLTrainer.__init__(
self,
parameters,
use_gpu,
additional_feature_types,
metrics_to_score,
gradient_handler,
)
self.q_network = self._get_model(
parameters.training, parameters.rainbow.dueling_architecture)
self.q_network_target = deepcopy(self.q_network)
self._set_optimizer(parameters.training.optimizer)
self.q_network_optimizer = self.optimizer_func(
self.q_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
self.reward_network = FullyConnectedNetwork(
reward_network_layers, parameters.training.activations)
self.reward_network_optimizer = self.optimizer_func(
self.reward_network.parameters(),
lr=parameters.training.learning_rate)
if self.use_gpu:
self.q_network.cuda()
self.q_network_target.cuda()
self.reward_network.cuda()
if use_all_avail_gpus:
self.q_network = torch.nn.DataParallel(self.q_network)
self.q_network_target = torch.nn.DataParallel(
self.q_network_target)
self.reward_network = torch.nn.DataParallel(
self.reward_network)
def _get_model(self, training_parameters, dueling_architecture=False):
if dueling_architecture:
return DuelingQNetwork(
training_parameters.layers,
training_parameters.activations,
action_dim=self.num_action_features,
)
elif training_parameters.factorization_parameters is None:
return FullyConnectedNetwork(
training_parameters.layers,
training_parameters.activations,
use_noisy_linear_layers=training_parameters.
use_noisy_linear_layers,
)
else:
return ParametricInnerProduct(
FullyConnectedNetwork(
training_parameters.factorization_parameters.state.layers,
training_parameters.factorization_parameters.state.
activations,
),
FullyConnectedNetwork(
training_parameters.factorization_parameters.action.layers,
training_parameters.factorization_parameters.action.
activations,
),
self.num_state_features,
self.num_action_features,
)
def get_detached_q_values(
self, state_action_pairs) -> Tuple[torch.Tensor, torch.Tensor]:
""" Gets the q values from the model and target networks """
with torch.no_grad():
q_values = self.q_network(state_action_pairs)
q_values_target = self.q_network_target(state_action_pairs)
return q_values, q_values_target
def train(self, training_samples: TrainingDataPage) -> None:
if self.minibatch == 0:
# Assume that the tensors are the right shape after the first minibatch
max_num_actions = training_samples.possible_next_actions_mask.shape[
1]
assert (training_samples.states.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.states.shape)
assert (training_samples.next_states.shape ==
training_samples.states.shape), "Invalid shape: " + str(
training_samples.next_states.shape)
assert (training_samples.not_terminal.shape ==
training_samples.rewards.shape), "Invalid shape: " + str(
training_samples.not_terminal.shape)
assert (training_samples.actions.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.actions.shape)
assert (training_samples.possible_next_actions_mask.shape[0] ==
self.minibatch_size), "Invalid shape: " + str(
training_samples.possible_next_actions_mask.shape)
assert training_samples.actions.shape[
1] == self.num_action_features, (
"Invalid shape: " +
str(training_samples.actions.shape[1]) + " != " +
str(self.num_action_features))
assert (
training_samples.possible_next_actions_state_concat.shape[0] ==
self.minibatch_size *
max_num_actions), ("Invalid shape: " + str(
training_samples.possible_next_actions_state_concat.shape)
+ " != " +
str(self.minibatch_size * max_num_actions))
self.minibatch += 1
states = training_samples.states.detach().requires_grad_(True)
actions = training_samples.actions
state_action_pairs = torch.cat((states, actions), dim=1)
rewards = training_samples.rewards
discount_tensor = torch.full(training_samples.time_diffs.shape,
self.gamma).type(self.dtype)
not_done_mask = training_samples.not_terminal
if self.use_seq_num_diff_as_time_diff:
discount_tensor = discount_tensor.pow(training_samples.time_diffs)
if self.maxq_learning:
all_next_q_values, all_next_q_values_target = self.get_detached_q_values(
training_samples.possible_next_actions_state_concat)
# Compute max a' Q(s', a') over all possible actions using target network
next_q_values, _ = self.get_max_q_values(
all_next_q_values,
all_next_q_values_target,
training_samples.possible_next_actions_mask,
)
else:
# SARSA
next_q_values, _ = self.get_detached_q_values(
torch.cat((training_samples.next_states,
training_samples.next_actions),
dim=1))
assert next_q_values.shape == not_done_mask.shape, (
"Invalid shapes: " + str(next_q_values.shape) + " != " +
str(not_done_mask.shape))
filtered_max_q_vals = next_q_values * not_done_mask
if self.minibatch < self.reward_burnin:
target_q_values = rewards
else:
assert discount_tensor.shape == filtered_max_q_vals.shape, (
"Invalid shapes: " + str(discount_tensor.shape) + " != " +
str(filtered_max_q_vals.shape))
target_q_values = rewards + (discount_tensor * filtered_max_q_vals)
# Get Q-value of action taken
q_values = self.q_network(state_action_pairs)
all_action_scores = q_values.detach()
self.model_values_on_logged_actions = q_values.detach()
value_loss = self.q_network_loss(q_values, target_q_values)
self.loss = value_loss.detach()
self.q_network_optimizer.zero_grad()
value_loss.backward()
if self.gradient_handler:
self.gradient_handler(self.q_network.parameters())
self.q_network_optimizer.step()
if self.minibatch < self.reward_burnin:
# Reward burnin: force target network
self._soft_update(self.q_network, self.q_network_target, 1.0)
else:
# Use the soft update rule to update target network
self._soft_update(self.q_network, self.q_network_target, self.tau)
# get reward estimates
reward_estimates = self.reward_network(state_action_pairs)
reward_loss = F.mse_loss(reward_estimates, rewards)
self.reward_network_optimizer.zero_grad()
reward_loss.backward()
self.reward_network_optimizer.step()
self.loss_reporter.report(
td_loss=self.loss,
reward_loss=reward_loss,
model_values_on_logged_actions=all_action_scores,
)
def predictor(self) -> ParametricDQNPredictor:
"""Builds a ParametricDQNPredictor."""
return ParametricDQNPredictor.export(
self,
self.state_normalization_parameters,
self.action_normalization_parameters,
self._additional_feature_types.int_features,
self.use_gpu,
)
def export(self) -> ParametricDQNPredictor:
return self.predictor()
class ParametricDQNTrainer(DQNTrainerBase):
def __init__(
self,
parameters: ContinuousActionModelParameters,
state_normalization_parameters: Dict[int, NormalizationParameters],
action_normalization_parameters: Dict[int, NormalizationParameters],
use_gpu: bool = False,
additional_feature_types: AdditionalFeatureTypes = DEFAULT_ADDITIONAL_FEATURE_TYPES,
metrics_to_score=None,
gradient_handler=None,
use_all_avail_gpus: bool = False,
) -> None:
self.double_q_learning = parameters.rainbow.double_q_learning
self.warm_start_model_path = parameters.training.warm_start_model_path
self.minibatch_size = parameters.training.minibatch_size
self.state_normalization_parameters = state_normalization_parameters
self.action_normalization_parameters = action_normalization_parameters
self.num_state_features = get_num_output_features(
state_normalization_parameters
)
self.num_action_features = get_num_output_features(
action_normalization_parameters
)
self.num_features = self.num_state_features + self.num_action_features
# ensure state and action IDs have no intersection
overlapping_features = set(state_normalization_parameters.keys()) & set(
action_normalization_parameters.keys()
)
assert len(overlapping_features) == 0, (
"There are some overlapping state and action features: "
+ str(overlapping_features)
)
reward_network_layers = deepcopy(parameters.training.layers)
reward_network_layers[0] = self.num_features
reward_network_layers[-1] = 1
if parameters.rainbow.dueling_architecture:
parameters.training.layers[0] = self.num_state_features
parameters.training.layers[-1] = 1
elif parameters.training.factorization_parameters is None:
parameters.training.layers[0] = self.num_features
parameters.training.layers[-1] = 1
else:
parameters.training.factorization_parameters.state.layers[
0
] = self.num_state_features
parameters.training.factorization_parameters.action.layers[
0
] = self.num_action_features
RLTrainer.__init__(
self,
parameters,
use_gpu,
additional_feature_types,
metrics_to_score,
gradient_handler,
)
self.q_network = self._get_model(
parameters.training, parameters.rainbow.dueling_architecture
)
self.q_network_target = deepcopy(self.q_network)
self._set_optimizer(parameters.training.optimizer)
self.q_network_optimizer = self.optimizer_func(
self.q_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
self.reward_network = FullyConnectedNetwork(
reward_network_layers, parameters.training.activations
)
self.reward_network_optimizer = self.optimizer_func(
self.reward_network.parameters(), lr=parameters.training.learning_rate
)
if self.use_gpu:
self.q_network.cuda()
self.q_network_target.cuda()
self.reward_network.cuda()
if use_all_avail_gpus:
self.q_network = torch.nn.DataParallel(self.q_network)
self.q_network_target = torch.nn.DataParallel(self.q_network_target)
self.reward_network = torch.nn.DataParallel(self.reward_network)
def _get_model(self, training_parameters, dueling_architecture=False):
if dueling_architecture:
return DuelingQNetwork(
training_parameters.layers,
training_parameters.activations,
action_dim=self.num_action_features,
)
elif training_parameters.factorization_parameters is None:
return FullyConnectedNetwork(
training_parameters.layers,
training_parameters.activations,
use_noisy_linear_layers=training_parameters.use_noisy_linear_layers,
)
else:
return ParametricInnerProduct(
FullyConnectedNetwork(
training_parameters.factorization_parameters.state.layers,
training_parameters.factorization_parameters.state.activations,
),
FullyConnectedNetwork(
training_parameters.factorization_parameters.action.layers,
training_parameters.factorization_parameters.action.activations,
),
self.num_state_features,
self.num_action_features,
)
def get_detached_q_values(
self, state_action_pairs
) -> Tuple[torch.Tensor, torch.Tensor]:
""" Gets the q values from the model and target networks """
with torch.no_grad():
q_values = self.q_network(state_action_pairs)
q_values_target = self.q_network_target(state_action_pairs)
return q_values, q_values_target
def train(self, training_samples: TrainingDataPage) -> None:
if self.minibatch == 0:
# Assume that the tensors are the right shape after the first minibatch
max_num_actions = training_samples.possible_next_actions_mask.shape[1]
assert (
training_samples.states.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.states.shape)
assert (
training_samples.next_states.shape == training_samples.states.shape
), "Invalid shape: " + str(training_samples.next_states.shape)
assert (
training_samples.not_terminal.shape == training_samples.rewards.shape
), "Invalid shape: " + str(training_samples.not_terminal.shape)
assert (
training_samples.actions.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.actions.shape)
assert (
training_samples.possible_next_actions_mask.shape[0]
== self.minibatch_size
), "Invalid shape: " + str(
training_samples.possible_next_actions_mask.shape
)
assert training_samples.actions.shape[1] == self.num_action_features, (
"Invalid shape: "
+ str(training_samples.actions.shape[1])
+ " != "
+ str(self.num_action_features)
)
assert (
training_samples.possible_next_actions_state_concat.shape[0]
== self.minibatch_size * max_num_actions
), (
"Invalid shape: "
+ str(training_samples.possible_next_actions_state_concat.shape)
+ " != "
+ str(self.minibatch_size * max_num_actions)
)
self.minibatch += 1
states = training_samples.states.detach().requires_grad_(True)
actions = training_samples.actions
state_action_pairs = torch.cat((states, actions), dim=1)
rewards = training_samples.rewards
discount_tensor = torch.full(
training_samples.time_diffs.shape, self.gamma
).type(self.dtype)
not_done_mask = training_samples.not_terminal
if self.use_seq_num_diff_as_time_diff:
discount_tensor = discount_tensor.pow(training_samples.time_diffs)
if self.maxq_learning:
all_next_q_values, all_next_q_values_target = self.get_detached_q_values(
training_samples.possible_next_actions_state_concat
)
# Compute max a' Q(s', a') over all possible actions using target network
next_q_values, _ = self.get_max_q_values_with_target(
all_next_q_values,
all_next_q_values_target,
training_samples.possible_next_actions_mask,
)
else:
# SARSA
next_q_values, _ = self.get_detached_q_values(
torch.cat(
(training_samples.next_states, training_samples.next_actions), dim=1
)
)
assert next_q_values.shape == not_done_mask.shape, (
"Invalid shapes: "
+ str(next_q_values.shape)
+ " != "
+ str(not_done_mask.shape)
)
filtered_max_q_vals = next_q_values * not_done_mask
if self.minibatch < self.reward_burnin:
target_q_values = rewards
else:
assert discount_tensor.shape == filtered_max_q_vals.shape, (
"Invalid shapes: "
+ str(discount_tensor.shape)
+ " != "
+ str(filtered_max_q_vals.shape)
)
target_q_values = rewards + (discount_tensor * filtered_max_q_vals)
# Get Q-value of action taken
q_values = self.q_network(state_action_pairs)
all_action_scores = q_values.detach()
self.model_values_on_logged_actions = q_values.detach()
value_loss = self.q_network_loss(q_values, target_q_values)
self.loss = value_loss.detach()
self.q_network_optimizer.zero_grad()
value_loss.backward()
if self.gradient_handler:
self.gradient_handler(self.q_network.parameters())
self.q_network_optimizer.step()
if self.minibatch < self.reward_burnin:
# Reward burnin: force target network
self._soft_update(self.q_network, self.q_network_target, 1.0)
else:
# Use the soft update rule to update target network
self._soft_update(self.q_network, self.q_network_target, self.tau)
# get reward estimates
reward_estimates = self.reward_network(state_action_pairs)
reward_loss = F.mse_loss(reward_estimates, rewards)
self.reward_network_optimizer.zero_grad()
reward_loss.backward()
self.reward_network_optimizer.step()
self.loss_reporter.report(
td_loss=self.loss,
reward_loss=reward_loss,
model_values_on_logged_actions=all_action_scores,
)
def predictor(self) -> ParametricDQNPredictor:
"""Builds a ParametricDQNPredictor."""
return ParametricDQNPredictor.export(
self,
self.state_normalization_parameters,
self.action_normalization_parameters,
self._additional_feature_types.int_features,
self.use_gpu,
)
def export(self) -> ParametricDQNPredictor:
return self.predictor()
