def input_prototype(self):
return rlt.PreprocessedStateAction(
state=rlt.FeatureVector(
float_features=torch.randn(1, 1, self.state_dim)),
action=rlt.FeatureVector(
float_features=torch.randn(1, 1, self.action_dim)),
)
def get_detached_q_values(
self, state, action) -> Tuple[rlt.SingleQValue, rlt.SingleQValue]:
""" Gets the q values from the model and target networks """
input = rlt.PreprocessedStateAction(state=state, action=action)
q_values = self.q_network(input)
q_values_target = self.q_network_target(input)
return q_values.q_value, q_values_target.q_value
def forward(self, input):
preprocessed_state = (self.state_preprocessor(input.state)
if self.state_preprocessor else input.state)
preprocessed_action = (self.action_preprocessor(input.action)
if self.action_preprocessor else input.action)
return self.q_network(
rlt.PreprocessedStateAction(state=preprocessed_state,
action=preprocessed_action))
def train(self, training_batch: rlt.PreprocessedTrainingBatch):
learning_input = training_batch.training_input
assert isinstance(learning_input, rlt.PreprocessedSlateQInput)
self.minibatch += 1
reward = learning_input.reward
reward_mask = learning_input.reward_mask
not_done_mask = learning_input.not_terminal
discount_tensor = torch.full_like(reward, self.gamma)
if self.maxq_learning:
raise NotImplementedError("Q-Learning for SlateQ is not implemented")
else:
# SARSA (Use the target network)
next_q_values = self.get_detached_q_values_target(
learning_input.tiled_next_state, learning_input.next_action
)
filtered_max_q_vals = next_q_values * not_done_mask.float()
target_q_values = reward + (discount_tensor * filtered_max_q_vals)
target_q_values = target_q_values[reward_mask]
with torch.enable_grad():
# Get Q-value of action taken
current_state_action = rlt.PreprocessedStateAction(
state=learning_input.tiled_state.as_preprocessed_feature_vector(),
action=learning_input.action.as_preprocessed_feature_vector(),
)
q_values = self.q_network(current_state_action).q_value.view(
*reward_mask.shape
)[reward_mask]
all_action_scores = q_values.detach()
value_loss = self.q_network_loss(q_values, target_q_values)
td_loss = value_loss.detach()
value_loss.backward()
self._maybe_run_optimizer(
self.q_network_optimizer, self.minibatches_per_step
)
# Use the soft update rule to update target network
self._maybe_soft_update(
self.q_network, self.q_network_target, self.tau, self.minibatches_per_step
)
self.loss_reporter.report(
td_loss=td_loss, model_values_on_logged_actions=all_action_scores
)
def get_slate_q_value(
self,
q_network,
tiled_state: rlt.PreprocessedTiledFeatureVector,
action: rlt.PreprocessedSlateFeatureVector,
) -> torch.Tensor:
""" Gets the q values from the model and target networks """
input = rlt.PreprocessedStateAction(
state=tiled_state.as_preprocessed_feature_vector(),
action=action.as_preprocessed_feature_vector(),
)
q_value = self.q_network_target(input).q_value
q_value = (q_value.view(action.float_features.shape[0],
action.float_features.shape[1]) *
action.item_mask * action.item_probability)
return q_value.sum(dim=1, keepdim=True)
def acc_rewards_of_one_solution(
self, init_state: torch.Tensor, solution: torch.Tensor, solution_idx: int
):
"""
ensemble_pop_size trajectories will be sampled to evaluate a
CEM solution. Each trajectory is generated by one world model
:param init_state: its shape is (state_dim, )
:param solution: its shape is (plan_horizon_length, action_dim)
:param solution_idx: the index of the solution
:return reward: Reward of each of ensemble_pop_size trajectories
"""
reward_matrix = np.zeros((self.ensemble_pop_size, self.plan_horizon_length))
for i in range(self.ensemble_pop_size):
state = init_state
mem_net_idx = np.random.randint(0, len(self.mem_net_list))
for j in range(self.plan_horizon_length):
# world_model_input.state shape:
# (1, 1, state_dim)
# world_model_input.action shape:
# (1, 1, action_dim)
world_model_input = rlt.PreprocessedStateAction(
state=rlt.PreprocessedFeatureVector(
float_features=state.reshape((1, 1, self.state_dim))
),
action=rlt.PreprocessedFeatureVector(
float_features=solution[j, :].reshape((1, 1, self.action_dim))
),
)
reward, next_state, not_terminal, not_terminal_prob = self.sample_reward_next_state_terminal(
world_model_input, self.mem_net_list[mem_net_idx]
)
reward_matrix[i, j] = reward * (self.gamma ** j)
if not not_terminal:
logger.debug(
f"Solution {solution_idx}: predict terminal at step {j}"
f" with prob. {1.0 - not_terminal_prob}"
)
if not not_terminal:
break
state = next_state
return np.sum(reward_matrix, axis=1)
def get_loss(
self,
training_batch: rlt.PreprocessedTrainingBatch,
state_dim: Optional[int] = None,
batch_first: bool = False,
):
"""
Compute losses:
GMMLoss(next_state, GMMPredicted) / (STATE_DIM + 2)
+ MSE(reward, predicted_reward)
+ BCE(not_terminal, logit_not_terminal)
The STATE_DIM + 2 factor is here to counteract the fact that the GMMLoss scales
approximately linearly with STATE_DIM, the feature size of states. All losses
are averaged both on the batch and the sequence dimensions (the two first
dimensions).
:param training_batch:
training_batch.learning_input has these fields:
- state: (BATCH_SIZE, SEQ_LEN, STATE_DIM) torch tensor
- action: (BATCH_SIZE, SEQ_LEN, ACTION_DIM) torch tensor
- reward: (BATCH_SIZE, SEQ_LEN) torch tensor
- not-terminal: (BATCH_SIZE, SEQ_LEN) torch tensor
- next_state: (BATCH_SIZE, SEQ_LEN, STATE_DIM) torch tensor
the first two dimensions may be swapped depending on batch_first
:param state_dim: the dimension of states. If provided, use it to normalize
gmm loss
:param batch_first: whether data's first dimension represents batch size. If
FALSE, state, action, reward, not-terminal, and next_state's first
two dimensions are SEQ_LEN and BATCH_SIZE.
:returns: dictionary of losses, containing the gmm, the mse, the bce and
the averaged loss.
"""
learning_input = training_batch.training_input
assert isinstance(learning_input, rlt.PreprocessedMemoryNetworkInput)
# mdnrnn's input should have seq_len as the first dimension
if batch_first:
state, action, next_state, reward, not_terminal = transpose(
learning_input.state.float_features,
learning_input.action,
learning_input.next_state.float_features,
learning_input.reward,
learning_input.not_terminal, # type: ignore
)
learning_input = rlt.PreprocessedMemoryNetworkInput( # type: ignore
state=rlt.PreprocessedFeatureVector(float_features=state),
reward=reward,
time_diff=torch.ones_like(reward).float(),
action=action,
not_terminal=not_terminal,
next_state=rlt.PreprocessedFeatureVector(
float_features=next_state),
step=None,
)
mdnrnn_input = rlt.PreprocessedStateAction(
state=learning_input.state, # type: ignore
action=rlt.PreprocessedFeatureVector(
float_features=learning_input.action), # type: ignore
)
mdnrnn_output = self.mdnrnn(mdnrnn_input)
mus, sigmas, logpi, rs, nts = (
mdnrnn_output.mus,
mdnrnn_output.sigmas,
mdnrnn_output.logpi,
mdnrnn_output.reward,
mdnrnn_output.not_terminal,
)
next_state = learning_input.next_state.float_features
not_terminal = learning_input.not_terminal # type: ignore
reward = learning_input.reward
if self.params.fit_only_one_next_step:
next_state, not_terminal, reward, mus, sigmas, logpi, nts, rs = tuple(
map(
lambda x: x[-1:],
(next_state, not_terminal, reward, mus, sigmas, logpi, nts,
rs),
))
gmm = (gmm_loss(next_state, mus, sigmas, logpi) *
self.params.next_state_loss_weight)
bce = (F.binary_cross_entropy_with_logits(nts, not_terminal) *
self.params.not_terminal_loss_weight)
mse = F.mse_loss(rs, reward) * self.params.reward_loss_weight
if state_dim is not None:
loss = gmm / (state_dim + 2) + bce + mse
else:
loss = gmm + bce + mse
return {"gmm": gmm, "bce": bce, "mse": mse, "loss": loss}
def create_from_tensors_parametric_dqn(
cls,
trainer: ParametricDQNTrainer,
mdp_ids: np.ndarray,
sequence_numbers: torch.Tensor,
states: rlt.PreprocessedFeatureVector,
actions: rlt.PreprocessedFeatureVector,
propensities: torch.Tensor,
rewards: torch.Tensor,
possible_actions_mask: torch.Tensor,
possible_actions: rlt.PreprocessedFeatureVector,
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)
state_action_pairs = rlt.PreprocessedStateAction(state=states, action=actions)
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.PreprocessedStateAction(
state=rlt.PreprocessedFeatureVector(float_features=tiled_state),
action=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
).q_value # type: ignore
optimal_q_values, _ = trainer.get_detached_q_values(
possible_actions_state_concat.state, possible_actions_state_concat.action
)
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
).q_value # type: ignore
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(state_action_pairs).q_value
model_rewards_and_metrics_for_logged_action = trainer.reward_network(
state_action_pairs
).q_value
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) # type: ignore
trainer.reward_network.train(old_reward_train_state) # type: ignore
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 train(self, training_batch) -> None:
if isinstance(training_batch, TrainingDataPage):
training_batch = training_batch.as_parametric_maxq_training_batch()
learning_input = training_batch.training_input
self.minibatch += 1
reward = learning_input.reward
not_done_mask = learning_input.not_terminal
discount_tensor = torch.full_like(reward, self.gamma)
if self.use_seq_num_diff_as_time_diff:
assert self.multi_steps is None
discount_tensor = torch.pow(self.gamma,
learning_input.time_diff.float())
if self.multi_steps is not None:
discount_tensor = torch.pow(self.gamma,
learning_input.step.float())
if self.maxq_learning:
all_next_q_values, all_next_q_values_target = self.get_detached_q_values(
learning_input.tiled_next_state,
learning_input.possible_next_actions)
# 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,
learning_input.possible_next_actions_mask.float(),
)
else:
# SARSA (Use the target network)
_, next_q_values = self.get_detached_q_values(
learning_input.next_state, learning_input.next_action)
filtered_max_q_vals = next_q_values * not_done_mask.float()
target_q_values = reward + (discount_tensor * filtered_max_q_vals)
with torch.enable_grad():
# Get Q-value of action taken
current_state_action = rlt.PreprocessedStateAction(
state=learning_input.state, action=learning_input.action)
q_values = self.q_network(current_state_action).q_value
self.all_action_scores = q_values.detach()
value_loss = self.q_network_loss(q_values, target_q_values)
self.loss = value_loss.detach()
value_loss.backward()
self._maybe_run_optimizer(self.q_network_optimizer,
self.minibatches_per_step)
# Use the soft update rule to update target network
self._maybe_soft_update(self.q_network, self.q_network_target,
self.tau, self.minibatches_per_step)
with torch.enable_grad():
if training_batch.extras.metrics is not None:
metrics_reward_concat_real_vals = torch.cat(
(reward, training_batch.extras.metrics), dim=1)
else:
metrics_reward_concat_real_vals = reward
# get reward estimates
reward_estimates = self.reward_network(
current_state_action).q_value
reward_loss = F.mse_loss(reward_estimates,
metrics_reward_concat_real_vals)
reward_loss.backward()
self._maybe_run_optimizer(self.reward_network_optimizer,
self.minibatches_per_step)
self.loss_reporter.report(
td_loss=self.loss,
reward_loss=reward_loss,
logged_rewards=reward,
model_values_on_logged_actions=self.all_action_scores,
)
def train(self, training_batch) -> None:
"""
IMPORTANT: the input action here is assumed to be preprocessed to match the
range of the output of the actor.
"""
if hasattr(training_batch, "as_policy_network_training_batch"):
training_batch = training_batch.as_policy_network_training_batch()
learning_input = training_batch.training_input
self.minibatch += 1
state = learning_input.state
action = learning_input.action
next_state = learning_input.next_state
reward = learning_input.reward
not_done_mask = learning_input.not_terminal
action = self._maybe_scale_action_in_train(action.float_features)
max_action = (self.max_action_range_tensor_training
if self.max_action_range_tensor_training else torch.ones(
action.shape, device=self.device))
min_action = (self.min_action_range_tensor_serving
if self.min_action_range_tensor_serving else
-torch.ones(action.shape, device=self.device))
# Compute current value estimates
current_state_action = rlt.PreprocessedStateAction(
state=state,
action=rlt.PreprocessedFeatureVector(float_features=action))
q1_value = self.q1_network(current_state_action).q_value
if self.q2_network:
q2_value = self.q2_network(current_state_action).q_value
actor_action = self.actor_network(
rlt.PreprocessedState(state=state)).action
# Generate target = r + y * min (Q1(s',pi(s')), Q2(s',pi(s')))
with torch.no_grad():
next_actor = self.actor_network_target(
rlt.PreprocessedState(state=next_state)).action
next_actor += (torch.randn_like(next_actor) *
self.target_policy_smoothing).clamp(
-self.noise_clip, self.noise_clip)
next_actor = torch.max(torch.min(next_actor, max_action),
min_action)
next_state_actor = rlt.PreprocessedStateAction(
state=next_state,
action=rlt.PreprocessedFeatureVector(
float_features=next_actor),
)
next_state_value = self.q1_network_target(next_state_actor).q_value
if self.q2_network is not None:
next_state_value = torch.min(
next_state_value,
self.q2_network_target(next_state_actor).q_value)
target_q_value = (
reward + self.gamma * next_state_value * not_done_mask.float())
# Optimize Q1 and Q2
q1_loss = F.mse_loss(q1_value, target_q_value)
q1_loss.backward()
self._maybe_run_optimizer(self.q1_network_optimizer,
self.minibatches_per_step)
if self.q2_network:
q2_loss = F.mse_loss(q2_value, target_q_value)
q2_loss.backward()
self._maybe_run_optimizer(self.q2_network_optimizer,
self.minibatches_per_step)
# Only update actor and target networks after a fixed number of Q updates
if self.minibatch % self.delayed_policy_update == 0:
actor_loss = -self.q1_network(
rlt.PreprocessedStateAction(
state=state,
action=rlt.PreprocessedFeatureVector(
float_features=actor_action),
)).q_value.mean()
actor_loss.backward()
self._maybe_run_optimizer(self.actor_network_optimizer,
self.minibatches_per_step)
# Use the soft update rule to update the target networks
self._maybe_soft_update(
self.q1_network,
self.q1_network_target,
self.tau,
self.minibatches_per_step,
)
self._maybe_soft_update(
self.actor_network,
self.actor_network_target,
self.tau,
self.minibatches_per_step,
)
if self.q2_network is not None:
self._maybe_soft_update(
self.q2_network,
self.q2_network_target,
self.tau,
self.minibatches_per_step,
)
# Logging at the end to schedule all the cuda operations first
if (self.tensorboard_logging_freq != 0
and self.minibatch % self.tensorboard_logging_freq == 0):
SummaryWriterContext.add_histogram("q1/logged_state_value",
q1_value)
if self.q2_network:
SummaryWriterContext.add_histogram("q2/logged_state_value",
q2_value)
SummaryWriterContext.add_histogram("q_network/next_state_value",
next_state_value)
SummaryWriterContext.add_histogram("q_network/target_q_value",
target_q_value)
SummaryWriterContext.add_histogram("actor/loss", actor_loss)
self.loss_reporter.report(
td_loss=float(q1_loss),
reward_loss=None,
logged_rewards=reward,
model_values_on_logged_actions=q1_value,
)
def create_from_tensors(
cls,
trainer: DQNTrainer,
mdp_ids: np.ndarray,
sequence_numbers: torch.Tensor,
states: rlt.PreprocessedFeatureVector,
actions: rlt.PreprocessedFeatureVector,
propensities: torch.Tensor,
rewards: torch.Tensor,
possible_actions_mask: torch.Tensor,
possible_actions: Optional[rlt.PreprocessedFeatureVector] = None,
max_num_actions: Optional[int] = None,
metrics: Optional[torch.Tensor] = None,
):
# Switch to evaluation mode for the network
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)
if max_num_actions:
# Parametric model CPE
state_action_pairs = rlt.PreprocessedStateAction(
state=states, action=actions
)
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.PreprocessedStateAction(
state=rlt.PreprocessedFeatureVector(float_features=tiled_state),
action=possible_actions,
)
# Parametric actions
# FIXME: model_values and model propensities should be calculated
# as in discrete dqn model
model_values = trainer.q_network(
possible_actions_state_concat
).q_value # type: ignore
optimal_q_values = model_values
eval_action_idxs = None
assert (
model_values.shape[0] * model_values.shape[1]
== 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)
model_propensities = masked_softmax(
model_values, possible_actions_mask, trainer.rl_temperature
)
model_rewards = trainer.reward_network(
possible_actions_state_concat
).q_value # type: ignore
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_values_for_logged_action = trainer.q_network(
state_action_pairs
).q_value
model_rewards_for_logged_action = trainer.reward_network(
state_action_pairs
).q_value
action_mask = (
torch.abs(model_values - model_values_for_logged_action) < 1e-3
).float()
model_metrics = None
model_metrics_for_logged_action = None
model_metrics_values = None
model_metrics_values_for_logged_action = None
else:
num_actions = trainer.num_actions
action_mask = actions.float() # type: ignore
# Switch to evaluation mode for the network
old_q_cpe_train_state = trainer.q_network_cpe.training
trainer.q_network_cpe.train(False)
# Discrete actions
rewards = trainer.boost_rewards(rewards, actions) # type: ignore
model_values = trainer.q_network_cpe(
rlt.PreprocessedState(state=states)
).q_values[:, 0:num_actions]
optimal_q_values = trainer.get_detached_q_values(
states # type: ignore
)[ # type: ignore
0
] # type: ignore
eval_action_idxs = trainer.get_max_q_values( # type: ignore
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, ( # type: ignore
"Invalid shape: "
+ str(model_values.shape) # type: ignore
+ " != "
+ str(actions.shape) # type: ignore
)
assert model_values.shape == possible_actions_mask.shape, ( # type: ignore
"Invalid shape: "
+ str(model_values.shape) # type: ignore
+ " != "
+ str(possible_actions_mask.shape) # type: ignore
)
model_values_for_logged_action = torch.sum(
model_values * action_mask, dim=1, keepdim=True
)
rewards_and_metric_rewards = trainer.reward_network(
rlt.PreprocessedState(state=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.q_values
model_rewards = rewards_and_metric_rewards[:, 0:num_actions]
assert model_rewards.shape == actions.shape, ( # type: ignore
"Invalid shape: "
+ str(model_rewards.shape) # type: ignore
+ " != "
+ str(actions.shape) # type: ignore
)
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(
rlt.PreprocessedState(state=states)
)
# Backward compatility
if hasattr(model_metrics_values, "q_values"):
model_metrics_values = model_metrics_values.q_values
model_metrics_values = model_metrics_values[:, num_actions:]
assert (
model_metrics_values.shape[1] == num_actions * num_metrics
), ( # type: ignore
"Invalid shape: "
+ str(model_metrics_values.shape[1]) # type: ignore
+ " != "
+ str(actions.shape[1] * num_metrics) # type: ignore
)
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
)
# Switch back to the old mode
trainer.q_network_cpe.train(old_q_cpe_train_state) # type: ignore
# Switch back to the old mode
trainer.q_network.train(old_q_train_state) # type: ignore
trainer.reward_network.train(old_reward_train_state) # type: ignore
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 train(self, training_batch) -> None:
"""
IMPORTANT: the input action here is assumed to be preprocessed to match the
range of the output of the actor.
"""
if hasattr(training_batch, "as_policy_network_training_batch"):
training_batch = training_batch.as_policy_network_training_batch()
learning_input = training_batch.training_input
self.minibatch += 1
state = learning_input.state
action = learning_input.action
reward = learning_input.reward
discount = torch.full_like(reward, self.gamma)
not_done_mask = learning_input.not_terminal
if self._should_scale_action_in_train():
action = action._replace(
float_features=rescale_torch_tensor(
action.float_features,
new_min=self.min_action_range_tensor_training,
new_max=self.max_action_range_tensor_training,
prev_min=self.min_action_range_tensor_serving,
prev_max=self.max_action_range_tensor_serving,
)
)
with torch.enable_grad():
#
# First, optimize Q networks; minimizing MSE between
# Q(s, a) & r + discount * V'(next_s)
#
current_state_action = rlt.PreprocessedStateAction(
state=state, action=action
)
q1_value = self.q1_network(current_state_action).q_value
if self.q2_network:
q2_value = self.q2_network(current_state_action).q_value
actor_output = self.actor_network(rlt.PreprocessedState(state=state))
# Optimize Alpha
if self.alpha_optimizer is not None:
alpha_loss = -(
self.log_alpha
* (actor_output.log_prob + self.target_entropy).detach()
).mean()
self.alpha_optimizer.zero_grad()
alpha_loss.backward()
self.alpha_optimizer.step()
self.entropy_temperature = self.log_alpha.exp()
with torch.no_grad():
if self.value_network is not None:
next_state_value = self.value_network_target(
learning_input.next_state.float_features
)
else:
next_state_actor_output = self.actor_network(
rlt.PreprocessedState(state=learning_input.next_state)
)
next_state_actor_action = rlt.PreprocessedStateAction(
state=learning_input.next_state,
action=rlt.PreprocessedFeatureVector(
float_features=next_state_actor_output.action
),
)
next_state_value = self.q1_network_target(
next_state_actor_action
).q_value
if self.q2_network is not None:
target_q2_value = self.q2_network_target(
next_state_actor_action
).q_value
next_state_value = torch.min(next_state_value, target_q2_value)
log_prob_a = self.actor_network.get_log_prob(
learning_input.next_state, next_state_actor_output.action
)
log_prob_a = log_prob_a.clamp(-20.0, 20.0)
next_state_value -= self.entropy_temperature * log_prob_a
target_q_value = (
reward + discount * next_state_value * not_done_mask.float()
)
q1_loss = F.mse_loss(q1_value, target_q_value)
q1_loss.backward()
self._maybe_run_optimizer(
self.q1_network_optimizer, self.minibatches_per_step
)
if self.q2_network:
q2_loss = F.mse_loss(q2_value, target_q_value)
q2_loss.backward()
self._maybe_run_optimizer(
self.q2_network_optimizer, self.minibatches_per_step
)
#
# Second, optimize the actor; minimizing KL-divergence between action propensity
# & softmax of value. Due to reparameterization trick, it ends up being
# log_prob(actor_action) - Q(s, actor_action)
#
state_actor_action = rlt.PreprocessedStateAction(
state=state,
action=rlt.PreprocessedFeatureVector(
float_features=actor_output.action
),
)
q1_actor_value = self.q1_network(state_actor_action).q_value
min_q_actor_value = q1_actor_value
if self.q2_network:
q2_actor_value = self.q2_network(state_actor_action).q_value
min_q_actor_value = torch.min(q1_actor_value, q2_actor_value)
actor_loss = (
self.entropy_temperature * actor_output.log_prob - min_q_actor_value
)
# Do this in 2 steps so we can log histogram of actor loss
actor_loss_mean = actor_loss.mean()
actor_loss_mean.backward()
self._maybe_run_optimizer(
self.actor_network_optimizer, self.minibatches_per_step
)
#
# Lastly, if applicable, optimize value network; minimizing MSE between
# V(s) & E_a~pi(s) [ Q(s,a) - log(pi(a|s)) ]
#
if self.value_network is not None:
state_value = self.value_network(state.float_features)
if self.logged_action_uniform_prior:
log_prob_a = torch.zeros_like(min_q_actor_value)
target_value = min_q_actor_value
else:
with torch.no_grad():
log_prob_a = actor_output.log_prob
log_prob_a = log_prob_a.clamp(-20.0, 20.0)
target_value = (
min_q_actor_value - self.entropy_temperature * log_prob_a
)
value_loss = F.mse_loss(state_value, target_value.detach())
value_loss.backward()
self._maybe_run_optimizer(
self.value_network_optimizer, self.minibatches_per_step
)
# Use the soft update rule to update the target networks
if self.value_network is not None:
self._maybe_soft_update(
self.value_network,
self.value_network_target,
self.tau,
self.minibatches_per_step,
)
else:
self._maybe_soft_update(
self.q1_network,
self.q1_network_target,
self.tau,
self.minibatches_per_step,
)
if self.q2_network is not None:
self._maybe_soft_update(
self.q2_network,
self.q2_network_target,
self.tau,
self.minibatches_per_step,
)
# Logging at the end to schedule all the cuda operations first
if (
self.tensorboard_logging_freq is not None
and self.minibatch % self.tensorboard_logging_freq == 0
):
SummaryWriterContext.add_histogram("q1/logged_state_value", q1_value)
if self.q2_network:
SummaryWriterContext.add_histogram("q2/logged_state_value", q2_value)
SummaryWriterContext.add_histogram("log_prob_a", log_prob_a)
if self.value_network:
SummaryWriterContext.add_histogram("value_network/target", target_value)
SummaryWriterContext.add_histogram(
"q_network/next_state_value", next_state_value
)
SummaryWriterContext.add_histogram(
"q_network/target_q_value", target_q_value
)
SummaryWriterContext.add_histogram(
"actor/min_q_actor_value", min_q_actor_value
)
SummaryWriterContext.add_histogram(
"actor/action_log_prob", actor_output.log_prob
)
SummaryWriterContext.add_histogram("actor/loss", actor_loss)
self.loss_reporter.report(
td_loss=float(q1_loss),
reward_loss=None,
logged_rewards=reward,
model_values_on_logged_actions=q1_value,
model_propensities=actor_output.log_prob.exp(),
model_values=min_q_actor_value,
)
