def as_cem_training_batch(self, batch_first=False):
"""
Generate one-step samples needed by CEM trainer.
The samples will be used to train an ensemble of world models used by CEM.
If batch_first = True:
state/next state shape: batch_size x 1 x state_dim
action shape: batch_size x 1 x action_dim
reward/terminal shape: batch_size x 1
else (default):
state/next state shape: 1 x batch_size x state_dim
action shape: 1 x batch_size x action_dim
reward/terminal shape: 1 x batch_size
"""
if batch_first:
seq_len_dim = 1
reward, not_terminal = self.rewards, self.not_terminal
else:
seq_len_dim = 0
reward, not_terminal = transpose(self.rewards, self.not_terminal)
training_input = rlt.PreprocessedMemoryNetworkInput(
state=rlt.PreprocessedFeatureVector(
float_features=self.states.unsqueeze(seq_len_dim)),
action=self.actions.unsqueeze(seq_len_dim),
next_state=rlt.PreprocessedFeatureVector(
float_features=self.next_states.unsqueeze(seq_len_dim)),
reward=reward,
not_terminal=not_terminal,
step=self.step,
time_diff=self.time_diffs,
)
return rlt.PreprocessedTrainingBatch(
training_input=training_input,
extras=rlt.ExtraData(
mdp_id=self.mdp_ids,
sequence_number=self.sequence_numbers,
action_probability=self.propensities,
max_num_actions=self.max_num_actions,
metrics=self.metrics,
),
)
def as_cem_training_batch(self):
"""
Generate one-step samples needed by CEM trainer.
The samples will be used to train an ensemble of world models used by CEM.
state/next state shape: 1 x batch_size x state_dim
action shape: 1 x batch_size x action_dim
reward/terminal shape: 1 x batch_size
"""
seq_len_dim = 0
reward, not_terminal = transpose(self.rewards, self.not_terminal)
return rlt.PreprocessedMemoryNetworkInput(
state=rlt.FeatureData(self.states.unsqueeze(seq_len_dim)),
action=self.actions.unsqueeze(seq_len_dim),
next_state=rlt.FeatureData(
float_features=self.next_states.unsqueeze(seq_len_dim)
),
reward=reward,
not_terminal=not_terminal,
step=self.step,
time_diff=self.time_diffs,
)
def evaluate(self, tdp: PreprocessedTrainingBatch):
""" Calculate state feature sensitivity due to actions:
randomly permutating actions and see how much the prediction of next
state feature deviates. """
mdnrnn_training_input = tdp.training_input
assert isinstance(mdnrnn_training_input,
PreprocessedMemoryNetworkInput)
self.trainer.mdnrnn.mdnrnn.eval()
batch_size, seq_len, state_dim = (
mdnrnn_training_input.next_state.float_features.size())
state_feature_num = self.state_feature_num
feature_sensitivity = torch.zeros(state_feature_num)
state, action, next_state, reward, not_terminal = transpose(
mdnrnn_training_input.state.float_features,
mdnrnn_training_input.action,
mdnrnn_training_input.next_state.float_features,
mdnrnn_training_input.reward,
mdnrnn_training_input.not_terminal,
)
mdnrnn_input = PreprocessedStateAction(
state=PreprocessedFeatureVector(float_features=state),
action=PreprocessedFeatureVector(float_features=action),
)
# the input of mdnrnn has seq-len as the first dimension
mdnrnn_output = self.trainer.mdnrnn(mdnrnn_input)
predicted_next_state_means = mdnrnn_output.mus
shuffled_mdnrnn_input = PreprocessedStateAction(
state=PreprocessedFeatureVector(float_features=state),
# shuffle the actions
action=PreprocessedFeatureVector(
float_features=action[:, torch.randperm(batch_size), :]),
)
shuffled_mdnrnn_output = self.trainer.mdnrnn(shuffled_mdnrnn_input)
shuffled_predicted_next_state_means = shuffled_mdnrnn_output.mus
assert (predicted_next_state_means.size() ==
shuffled_predicted_next_state_means.size() ==
(seq_len, batch_size, self.trainer.params.num_gaussians,
state_dim))
state_feature_boundaries = self.sorted_state_feature_start_indices + [
state_dim
]
for i in range(state_feature_num):
boundary_start, boundary_end = (
state_feature_boundaries[i],
state_feature_boundaries[i + 1],
)
abs_diff = torch.mean(
torch.sum(
torch.abs(
shuffled_predicted_next_state_means[:, :, :,
boundary_start:
boundary_end] -
predicted_next_state_means[:, :, :,
boundary_start:boundary_end]
),
dim=3,
))
feature_sensitivity[i] = abs_diff.cpu().detach().item()
self.trainer.mdnrnn.mdnrnn.train()
logger.info("**** Debug tool feature sensitivity ****: {}".format(
feature_sensitivity))
return {"feature_sensitivity": feature_sensitivity.numpy()}
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}