def actor_critic_loss(
policy: Policy, model: ModelV2,
dist_class: Type[TorchDistributionWrapper],
train_batch: SampleBatch) -> Union[TensorType, List[TensorType]]:
"""Constructs the loss for the Soft Actor Critic.
Args:
policy (Policy): The Policy to calculate the loss for.
model (ModelV2): The Model to calculate the loss for.
dist_class (Type[TorchDistributionWrapper]: The action distr. class.
train_batch (SampleBatch): The training data.
Returns:
Union[TensorType, List[TensorType]]: A single loss tensor or a list
of loss tensors.
"""
# Should be True only for debugging purposes (e.g. test cases)!
deterministic = policy.config["_deterministic_loss"]
i = 0
state_batches = []
while "state_in_{}".format(i) in train_batch:
state_batches.append(train_batch["state_in_{}".format(i)])
i += 1
assert state_batches
seq_lens = train_batch.get("seq_lens")
model_out_t, state_in_t = model(
{
"obs": train_batch[SampleBatch.CUR_OBS],
"prev_actions": train_batch[SampleBatch.PREV_ACTIONS],
"prev_rewards": train_batch[SampleBatch.PREV_REWARDS],
"is_training": True,
}, state_batches, seq_lens)
states_in_t = model.select_state(state_in_t, ["policy", "q", "twin_q"])
model_out_tp1, state_in_tp1 = model(
{
"obs": train_batch[SampleBatch.NEXT_OBS],
"prev_actions": train_batch[SampleBatch.ACTIONS],
"prev_rewards": train_batch[SampleBatch.REWARDS],
"is_training": True,
}, state_batches, seq_lens)
states_in_tp1 = model.select_state(state_in_tp1, ["policy", "q", "twin_q"])
target_model_out_tp1, target_state_in_tp1 = policy.target_model(
{
"obs": train_batch[SampleBatch.NEXT_OBS],
"prev_actions": train_batch[SampleBatch.ACTIONS],
"prev_rewards": train_batch[SampleBatch.REWARDS],
"is_training": True,
}, state_batches, seq_lens)
target_states_in_tp1 = \
policy.target_model.select_state(state_in_tp1,
["policy", "q", "twin_q"])
alpha = torch.exp(model.log_alpha)
# Discrete case.
if model.discrete:
# Get all action probs directly from pi and form their logp.
log_pis_t = F.log_softmax(model.get_policy_output(
model_out_t, states_in_t["policy"], seq_lens)[0],
dim=-1)
policy_t = torch.exp(log_pis_t)
log_pis_tp1 = F.log_softmax(
model.get_policy_output(model_out_tp1, states_in_tp1["policy"],
seq_lens)[0], -1)
policy_tp1 = torch.exp(log_pis_tp1)
# Q-values.
q_t = model.get_q_values(model_out_t, states_in_t["q"], seq_lens)[0]
# Target Q-values.
q_tp1 = policy.target_model.get_q_values(target_model_out_tp1,
target_states_in_tp1["q"],
seq_lens)[0]
if policy.config["twin_q"]:
twin_q_t = model.get_twin_q_values(model_out_t,
states_in_t["twin_q"],
seq_lens)[0]
twin_q_tp1 = policy.target_model.get_twin_q_values(
target_model_out_tp1, target_states_in_tp1["twin_q"],
seq_lens)[0]
q_tp1 = torch.min(q_tp1, twin_q_tp1)
q_tp1 -= alpha * log_pis_tp1
# Actually selected Q-values (from the actions batch).
one_hot = F.one_hot(train_batch[SampleBatch.ACTIONS].long(),
num_classes=q_t.size()[-1])
q_t_selected = torch.sum(q_t * one_hot, dim=-1)
if policy.config["twin_q"]:
twin_q_t_selected = torch.sum(twin_q_t * one_hot, dim=-1)
# Discrete case: "Best" means weighted by the policy (prob) outputs.
q_tp1_best = torch.sum(torch.mul(policy_tp1, q_tp1), dim=-1)
q_tp1_best_masked = \
(1.0 - train_batch[SampleBatch.DONES].float()) * \
q_tp1_best
# Continuous actions case.
else:
# Sample single actions from distribution.
action_dist_class = _get_dist_class(policy, policy.config,
policy.action_space)
action_dist_t = action_dist_class(
model.get_policy_output(model_out_t, states_in_t["policy"],
seq_lens)[0], policy.model)
policy_t = action_dist_t.sample() if not deterministic else \
action_dist_t.deterministic_sample()
log_pis_t = torch.unsqueeze(action_dist_t.logp(policy_t), -1)
action_dist_tp1 = action_dist_class(
model.get_policy_output(model_out_tp1, states_in_tp1["policy"],
seq_lens)[0], policy.model)
policy_tp1 = action_dist_tp1.sample() if not deterministic else \
action_dist_tp1.deterministic_sample()
log_pis_tp1 = torch.unsqueeze(action_dist_tp1.logp(policy_tp1), -1)
# Q-values for the actually selected actions.
q_t = model.get_q_values(model_out_t, states_in_t["q"], seq_lens,
train_batch[SampleBatch.ACTIONS])[0]
if policy.config["twin_q"]:
twin_q_t = model.get_twin_q_values(
model_out_t, states_in_t["twin_q"], seq_lens,
train_batch[SampleBatch.ACTIONS])[0]
# Q-values for current policy in given current state.
q_t_det_policy = model.get_q_values(model_out_t, states_in_t["q"],
seq_lens, policy_t)[0]
if policy.config["twin_q"]:
twin_q_t_det_policy = model.get_twin_q_values(
model_out_t, states_in_t["twin_q"], seq_lens, policy_t)[0]
q_t_det_policy = torch.min(q_t_det_policy, twin_q_t_det_policy)
# Target q network evaluation.
q_tp1 = policy.target_model.get_q_values(target_model_out_tp1,
target_states_in_tp1["q"],
seq_lens, policy_tp1)[0]
if policy.config["twin_q"]:
twin_q_tp1 = policy.target_model.get_twin_q_values(
target_model_out_tp1, target_states_in_tp1["twin_q"], seq_lens,
policy_tp1)[0]
# Take min over both twin-NNs.
q_tp1 = torch.min(q_tp1, twin_q_tp1)
q_t_selected = torch.squeeze(q_t, dim=-1)
if policy.config["twin_q"]:
twin_q_t_selected = torch.squeeze(twin_q_t, dim=-1)
q_tp1 -= alpha * log_pis_tp1
q_tp1_best = torch.squeeze(input=q_tp1, dim=-1)
q_tp1_best_masked = \
(1.0 - train_batch[SampleBatch.DONES].float()) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = (train_batch[SampleBatch.REWARDS] +
(policy.config["gamma"]**policy.config["n_step"]) *
q_tp1_best_masked).detach()
# BURNIN #
B = state_batches[0].shape[0]
T = q_t_selected.shape[0] // B
seq_mask = sequence_mask(train_batch["seq_lens"], T)
# Mask away also the burn-in sequence at the beginning.
burn_in = policy.config["burn_in"]
if burn_in > 0 and burn_in < T:
seq_mask[:, :burn_in] = False
seq_mask = seq_mask.reshape(-1)
num_valid = torch.sum(seq_mask)
def reduce_mean_valid(t):
return torch.sum(t[seq_mask]) / num_valid
# Compute the TD-error (potentially clipped).
base_td_error = torch.abs(q_t_selected - q_t_selected_target)
if policy.config["twin_q"]:
twin_td_error = torch.abs(twin_q_t_selected - q_t_selected_target)
td_error = 0.5 * (base_td_error + twin_td_error)
else:
td_error = base_td_error
critic_loss = [
reduce_mean_valid(train_batch[PRIO_WEIGHTS] *
huber_loss(base_td_error))
]
if policy.config["twin_q"]:
critic_loss.append(
reduce_mean_valid(train_batch[PRIO_WEIGHTS] *
huber_loss(twin_td_error)))
# Alpha- and actor losses.
# Note: In the papers, alpha is used directly, here we take the log.
# Discrete case: Multiply the action probs as weights with the original
# loss terms (no expectations needed).
if model.discrete:
weighted_log_alpha_loss = policy_t.detach() * (
-model.log_alpha * (log_pis_t + model.target_entropy).detach())
# Sum up weighted terms and mean over all batch items.
alpha_loss = reduce_mean_valid(
torch.sum(weighted_log_alpha_loss, dim=-1))
# Actor loss.
actor_loss = reduce_mean_valid(
torch.sum(
torch.mul(
# NOTE: No stop_grad around policy output here
# (compare with q_t_det_policy for continuous case).
policy_t,
alpha.detach() * log_pis_t - q_t.detach()),
dim=-1))
else:
alpha_loss = -reduce_mean_valid(
model.log_alpha * (log_pis_t + model.target_entropy).detach())
# Note: Do not detach q_t_det_policy here b/c is depends partly
# on the policy vars (policy sample pushed through Q-net).
# However, we must make sure `actor_loss` is not used to update
# the Q-net(s)' variables.
actor_loss = reduce_mean_valid(alpha.detach() * log_pis_t -
q_t_det_policy)
# Save for stats function.
policy.q_t = q_t * seq_mask[..., None]
policy.policy_t = policy_t * seq_mask[..., None]
policy.log_pis_t = log_pis_t * seq_mask[..., None]
# Store td-error in model, such that for multi-GPU, we do not override
# them during the parallel loss phase. TD-error tensor in final stats
# can then be concatenated and retrieved for each individual batch item.
model.td_error = td_error * seq_mask
policy.actor_loss = actor_loss
policy.critic_loss = critic_loss
policy.alpha_loss = alpha_loss
policy.log_alpha_value = model.log_alpha
policy.alpha_value = alpha
policy.target_entropy = model.target_entropy
# Return all loss terms corresponding to our optimizers.
return tuple([policy.actor_loss] + policy.critic_loss +
[policy.alpha_loss])
def action_distribution_fn(
policy: Policy,
model: ModelV2,
input_dict: ModelInputDict,
*,
state_batches: Optional[List[TensorType]] = None,
seq_lens: Optional[TensorType] = None,
prev_action_batch: Optional[TensorType] = None,
prev_reward_batch=None,
explore: Optional[bool] = None,
timestep: Optional[int] = None,
is_training: Optional[bool] = None) -> \
Tuple[TensorType, Type[TorchDistributionWrapper], List[TensorType]]:
"""The action distribution function to be used the algorithm.
An action distribution function is used to customize the choice of action
distribution class and the resulting action distribution inputs (to
parameterize the distribution object).
After parameterizing the distribution, a `sample()` call
will be made on it to generate actions.
Args:
policy (Policy): The Policy being queried for actions and calling this
function.
model (TorchModelV2): The SAC specific Model to use to generate the
distribution inputs (see sac_tf|torch_model.py). Must support the
`get_policy_output` method.
input_dict (ModelInputDict): The input-dict to be used for the model
call.
state_batches (Optional[List[TensorType]]): The list of internal state
tensor batches.
seq_lens (Optional[TensorType]): The tensor of sequence lengths used
in RNNs.
prev_action_batch (Optional[TensorType]): Optional batch of prev
actions used by the model.
prev_reward_batch (Optional[TensorType]): Optional batch of prev
rewards used by the model.
explore (Optional[bool]): Whether to activate exploration or not. If
None, use value of `config.explore`.
timestep (Optional[int]): An optional timestep.
is_training (Optional[bool]): An optional is-training flag.
Returns:
Tuple[TensorType, Type[TorchDistributionWrapper], List[TensorType]]:
The dist inputs, dist class, and a list of internal state outputs
(in the RNN case).
"""
# Get base-model output (w/o the SAC specific parts of the network).
model_out, state_in = model(input_dict, state_batches, seq_lens)
# Use the base output to get the policy outputs from the SAC model's
# policy components.
states_in = model.select_state(state_in, ["policy", "q", "twin_q"])
distribution_inputs, policy_state_out = \
model.get_policy_output(model_out, states_in["policy"], seq_lens)
_, q_state_out = model.get_q_values(model_out, states_in["q"], seq_lens)
if model.twin_q_net:
_, twin_q_state_out = \
model.get_twin_q_values(model_out, states_in["twin_q"], seq_lens)
else:
twin_q_state_out = []
# Get a distribution class to be used with the just calculated dist-inputs.
action_dist_class = _get_dist_class(policy, policy.config,
policy.action_space)
states_out = policy_state_out + q_state_out + twin_q_state_out
return distribution_inputs, action_dist_class, states_out
