def policy(
self,
states_tiled: torch.Tensor,
possible_actions_with_presence: Tuple[torch.Tensor, torch.Tensor],
):
possible_actions, possible_actions_presence = possible_actions_with_presence
assert states_tiled.size()[0] == possible_actions.size()[0]
assert possible_actions.size()[1] == self.action_dim
assert possible_actions.size()[0] == possible_actions_presence.size(
)[0]
if self.use_gpu:
states_tiled = states_tiled.cuda()
possible_actions = possible_actions.cuda()
q_scores = self.predict(states_tiled,
possible_actions).reshape([1, self.action_dim])
possible_actions_presence = (possible_actions_presence.sum(dim=1) >
0).float()
# set impossible actions so low that they can't be picked
q_scores -= (
1.0 - possible_actions_presence.reshape(1, self.action_dim)) * 1e10
q_scores_softmax_numpy = masked_softmax(
q_scores.reshape(1, -1),
possible_actions_presence.reshape(1, -1),
self.trainer.rl_temperature,
).numpy()[0]
if (np.isnan(q_scores_softmax_numpy).any()
or np.max(q_scores_softmax_numpy) < 1e-3):
q_scores_softmax_numpy[:] = 1.0 / q_scores_softmax_numpy.shape[0]
return DqnPolicyActionSet(
greedy=int(torch.argmax(q_scores)),
softmax=int(
np.random.choice(q_scores.size()[1],
p=q_scores_softmax_numpy)),
)
def _calculate_cpes(
self,
training_batch,
states,
next_states,
all_action_scores,
all_next_action_scores,
logged_action_idxs,
discount_tensor,
not_done_mask,
):
if not self.calc_cpe_in_training:
return
if training_batch.extras.metrics is None:
metrics_reward_concat_real_vals = training_batch.reward
else:
metrics_reward_concat_real_vals = torch.cat(
(training_batch.reward, training_batch.extras.metrics), dim=1)
model_propensities_next_states = masked_softmax(
all_next_action_scores,
training_batch.possible_next_actions_mask
if self.maxq_learning else training_batch.next_action,
self.rl_temperature,
)
######### 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)
yield reward_loss
######### Train separate q-network for CPE evaluation #############
metric_q_values = self.q_network_cpe(states).gather(
1, self.reward_idx_offsets + logged_action_idxs)
all_metrics_target_q_values = torch.chunk(
self.q_network_cpe_target(next_states).detach(),
len(self.metrics_to_score),
dim=1,
)
target_metric_q_values = []
for i, per_metric_target_q_values in enumerate(
all_metrics_target_q_values):
per_metric_next_q_values = torch.sum(
per_metric_target_q_values * model_propensities_next_states,
1,
keepdim=True,
)
per_metric_next_q_values = per_metric_next_q_values * not_done_mask
per_metric_target_q_values = metrics_reward_concat_real_vals[:, i:i + 1] + (
discount_tensor * per_metric_next_q_values)
target_metric_q_values.append(per_metric_target_q_values)
target_metric_q_values = torch.cat(target_metric_q_values, dim=1)
metric_q_value_loss = self.q_network_loss(metric_q_values,
target_metric_q_values)
model_propensities = masked_softmax(
all_action_scores,
training_batch.possible_actions_mask
if self.maxq_learning else training_batch.action,
self.rl_temperature,
)
# Extract rewards predicted by the reward_network. The other columns will
# give predicted values for other metrics, if such were specified.
model_rewards = reward_estimates[:,
torch.arange(
self.reward_idx_offsets[0],
self.reward_idx_offsets[0] +
self.num_actions,
), ]
self.reporter.log(
reward_loss=reward_loss,
model_propensities=model_propensities,
model_rewards=model_rewards,
)
yield metric_q_value_loss
def _calculate_cpes(
self,
training_batch,
states,
next_states,
all_action_scores,
all_next_action_scores,
logged_action_idxs,
discount_tensor,
not_done_mask,
):
if not self.calc_cpe_in_training:
return None, None, None
if training_batch.extras.metrics is None:
metrics_reward_concat_real_vals = training_batch.reward
else:
metrics_reward_concat_real_vals = torch.cat(
(training_batch.reward, training_batch.extras.metrics), dim=1)
model_propensities_next_states = masked_softmax(
all_next_action_scores,
training_batch.possible_next_actions_mask
if self.maxq_learning else training_batch.next_action,
self.rl_temperature,
)
with torch.enable_grad():
######### 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)
reward_loss.backward()
self._maybe_run_optimizer(self.reward_network_optimizer,
self.minibatches_per_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)
all_metrics_target_q_values = torch.chunk(
self.q_network_cpe_target(next_states).detach(),
len(self.metrics_to_score),
dim=1,
)
target_metric_q_values = []
for i, per_metric_target_q_values in enumerate(
all_metrics_target_q_values):
per_metric_next_q_values = torch.sum(
per_metric_target_q_values *
model_propensities_next_states,
1,
keepdim=True,
)
per_metric_next_q_values = per_metric_next_q_values * not_done_mask
per_metric_target_q_values = metrics_reward_concat_real_vals[:, i:i + 1] + (
discount_tensor * per_metric_next_q_values)
target_metric_q_values.append(per_metric_target_q_values)
target_metric_q_values = torch.cat(target_metric_q_values, dim=1)
metric_q_value_loss = self.q_network_loss(metric_q_values,
target_metric_q_values)
metric_q_value_loss.backward()
self._maybe_run_optimizer(self.q_network_cpe_optimizer,
self.minibatches_per_step)
# Use the soft update rule to update target network
self._maybe_soft_update(
self.q_network_cpe,
self.q_network_cpe_target,
self.tau,
self.minibatches_per_step,
)
model_propensities = masked_softmax(
all_action_scores,
training_batch.possible_actions_mask
if self.maxq_learning else training_batch.action,
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 create_from_tensors_dqn(
cls,
trainer: DQNTrainer,
mdp_ids: torch.Tensor,
sequence_numbers: torch.Tensor,
states: rlt.FeatureData,
actions: rlt.FeatureData,
propensities: torch.Tensor,
rewards: torch.Tensor,
possible_actions_mask: torch.Tensor,
metrics: Optional[torch.Tensor] = None,
):
old_q_train_state = trainer.q_network.training
# pyre-fixme[16]: `DQNTrainer` has no attribute `reward_network`.
old_reward_train_state = trainer.reward_network.training
# pyre-fixme[16]: `DQNTrainer` has no attribute `q_network_cpe`.
old_q_cpe_train_state = trainer.q_network_cpe.training
trainer.q_network.train(False)
trainer.reward_network.train(False)
trainer.q_network_cpe.train(False)
num_actions = trainer.num_actions
action_mask = actions.float()
rewards = trainer.boost_rewards(rewards, actions)
model_values = trainer.q_network_cpe(states)[:, 0:num_actions]
optimal_q_values, _ = trainer.get_detached_q_values(states)
# Do we ever really use eval_action_idxs?
eval_action_idxs = trainer.get_max_q_values(
optimal_q_values, possible_actions_mask
)[1]
model_propensities = masked_softmax(
optimal_q_values, possible_actions_mask, trainer.rl_temperature
)
assert model_values.shape == actions.shape, (
"Invalid shape: " + str(model_values.shape) + " != " + str(actions.shape)
)
assert model_values.shape == possible_actions_mask.shape, (
"Invalid shape: "
+ str(model_values.shape)
+ " != "
+ str(possible_actions_mask.shape)
)
model_values_for_logged_action = torch.sum(
model_values * action_mask, dim=1, keepdim=True
)
rewards_and_metric_rewards = trainer.reward_network(states)
# In case we reuse the modular for Q-network
if hasattr(rewards_and_metric_rewards, "q_values"):
rewards_and_metric_rewards = rewards_and_metric_rewards
model_rewards = rewards_and_metric_rewards[:, 0:num_actions]
assert model_rewards.shape == actions.shape, (
"Invalid shape: " + str(model_rewards.shape) + " != " + str(actions.shape)
)
model_rewards_for_logged_action = torch.sum(
model_rewards * action_mask, dim=1, keepdim=True
)
model_metrics = rewards_and_metric_rewards[:, num_actions:]
assert model_metrics.shape[1] % num_actions == 0, (
"Invalid metrics shape: "
+ str(model_metrics.shape)
+ " "
+ str(num_actions)
)
num_metrics = model_metrics.shape[1] // num_actions
if num_metrics == 0:
model_metrics_values = None
model_metrics_for_logged_action = None
model_metrics_values_for_logged_action = None
else:
model_metrics_values = trainer.q_network_cpe(states)
# Backward compatility
if hasattr(model_metrics_values, "q_values"):
model_metrics_values = model_metrics_values
model_metrics_values = model_metrics_values[:, num_actions:]
assert model_metrics_values.shape[1] == num_actions * num_metrics, (
"Invalid shape: "
+ str(model_metrics_values.shape[1])
+ " != "
+ str(actions.shape[1] * num_metrics)
)
model_metrics_for_logged_action_list = []
model_metrics_values_for_logged_action_list = []
for metric_index in range(num_metrics):
metric_start = metric_index * num_actions
metric_end = (metric_index + 1) * num_actions
model_metrics_for_logged_action_list.append(
torch.sum(
model_metrics[:, metric_start:metric_end] * action_mask,
dim=1,
keepdim=True,
)
)
model_metrics_values_for_logged_action_list.append(
torch.sum(
model_metrics_values[:, metric_start:metric_end] * action_mask,
dim=1,
keepdim=True,
)
)
model_metrics_for_logged_action = torch.cat(
model_metrics_for_logged_action_list, dim=1
)
model_metrics_values_for_logged_action = torch.cat(
model_metrics_values_for_logged_action_list, dim=1
)
trainer.q_network_cpe.train(old_q_cpe_train_state)
trainer.q_network.train(old_q_train_state)
trainer.reward_network.train(old_reward_train_state)
return cls(
mdp_id=mdp_ids,
sequence_number=sequence_numbers,
logged_propensities=propensities,
logged_rewards=rewards,
action_mask=action_mask,
model_rewards=model_rewards,
model_rewards_for_logged_action=model_rewards_for_logged_action,
model_values=model_values,
model_values_for_logged_action=model_values_for_logged_action,
model_metrics_values=model_metrics_values,
model_metrics_values_for_logged_action=model_metrics_values_for_logged_action,
model_propensities=model_propensities,
logged_metrics=metrics,
model_metrics=model_metrics,
model_metrics_for_logged_action=model_metrics_for_logged_action,
# Will compute later
logged_values=None,
logged_metrics_values=None,
possible_actions_mask=possible_actions_mask,
optimal_q_values=optimal_q_values,
eval_action_idxs=eval_action_idxs,
)
def create_from_tensors_parametric_dqn(
cls,
trainer: ParametricDQNTrainer,
mdp_ids: torch.Tensor,
sequence_numbers: torch.Tensor,
states: rlt.FeatureData,
actions: rlt.FeatureData,
propensities: torch.Tensor,
rewards: torch.Tensor,
possible_actions_mask: torch.Tensor,
possible_actions: rlt.FeatureData,
max_num_actions: int,
metrics: Optional[torch.Tensor] = None,
):
old_q_train_state = trainer.q_network.training
old_reward_train_state = trainer.reward_network.training
trainer.q_network.train(False)
trainer.reward_network.train(False)
tiled_state = states.float_features.repeat(1, max_num_actions).reshape(
-1, states.float_features.shape[1]
)
assert possible_actions is not None
# Get Q-value of action taken
possible_actions_state_concat = (rlt.FeatureData(tiled_state), possible_actions)
# FIXME: model_values, model_values_for_logged_action, and model_metrics_values
# should be calculated using q_network_cpe (as in discrete dqn).
# q_network_cpe has not been added in parametric dqn yet.
model_values = trainer.q_network(*possible_actions_state_concat)
optimal_q_values, _ = trainer.get_detached_q_values(
*possible_actions_state_concat
)
eval_action_idxs = None
assert (
model_values.shape[1] == 1
and model_values.shape[0]
== possible_actions_mask.shape[0] * possible_actions_mask.shape[1]
), (
"Invalid shapes: "
+ str(model_values.shape)
+ " != "
+ str(possible_actions_mask.shape)
)
model_values = model_values.reshape(possible_actions_mask.shape)
optimal_q_values = optimal_q_values.reshape(possible_actions_mask.shape)
model_propensities = masked_softmax(
optimal_q_values, possible_actions_mask, trainer.rl_temperature
)
rewards_and_metric_rewards = trainer.reward_network(
*possible_actions_state_concat
)
model_rewards = rewards_and_metric_rewards[:, :1]
assert (
model_rewards.shape[0] * model_rewards.shape[1]
== possible_actions_mask.shape[0] * possible_actions_mask.shape[1]
), (
"Invalid shapes: "
+ str(model_rewards.shape)
+ " != "
+ str(possible_actions_mask.shape)
)
model_rewards = model_rewards.reshape(possible_actions_mask.shape)
model_metrics = rewards_and_metric_rewards[:, 1:]
model_metrics = model_metrics.reshape(possible_actions_mask.shape[0], -1)
model_values_for_logged_action = trainer.q_network(states, actions)
model_rewards_and_metrics_for_logged_action = trainer.reward_network(
states, actions
)
model_rewards_for_logged_action = model_rewards_and_metrics_for_logged_action[
:, :1
]
action_dim = possible_actions.float_features.shape[1]
action_mask = torch.all(
possible_actions.float_features.view(-1, max_num_actions, action_dim)
== actions.float_features.unsqueeze(dim=1),
dim=2,
).float()
assert torch.all(action_mask.sum(dim=1) == 1)
num_metrics = model_metrics.shape[1] // max_num_actions
model_metrics_values = None
model_metrics_for_logged_action = None
model_metrics_values_for_logged_action = None
if num_metrics > 0:
# FIXME: calculate model_metrics_values when q_network_cpe is added
# to parametric dqn
model_metrics_values = model_values.repeat(1, num_metrics)
trainer.q_network.train(old_q_train_state)
trainer.reward_network.train(old_reward_train_state)
return cls(
mdp_id=mdp_ids,
sequence_number=sequence_numbers,
logged_propensities=propensities,
logged_rewards=rewards,
action_mask=action_mask,
model_rewards=model_rewards,
model_rewards_for_logged_action=model_rewards_for_logged_action,
model_values=model_values,
model_values_for_logged_action=model_values_for_logged_action,
model_metrics_values=model_metrics_values,
model_metrics_values_for_logged_action=model_metrics_values_for_logged_action,
model_propensities=model_propensities,
logged_metrics=metrics,
model_metrics=model_metrics,
model_metrics_for_logged_action=model_metrics_for_logged_action,
# Will compute later
logged_values=None,
logged_metrics_values=None,
possible_actions_mask=possible_actions_mask,
optimal_q_values=optimal_q_values,
eval_action_idxs=eval_action_idxs,
)
