discrete_bcq#
Source code: tianshou/policy/imitation/discrete_bcq.py
- class DiscreteBCQPolicy(*, model: Module, imitator: Module, optim: Optimizer, action_space: Discrete, discount_factor: float = 0.99, estimation_step: int = 1, target_update_freq: int = 8000, eval_eps: float = 0.001, unlikely_action_threshold: float = 0.3, imitation_logits_penalty: float = 0.01, reward_normalization: bool = False, is_double: bool = True, clip_loss_grad: bool = False, observation_space: Space | None = None, lr_scheduler: LRScheduler | MultipleLRSchedulers | None = None)[source]#
Implementation of discrete BCQ algorithm. arXiv:1910.01708.
- Parameters:
model – a model following the rules (s_B -> action_values_BA)
imitator – a model following the rules in
BasePolicy
. (s -> imitation_logits)optim – a torch.optim for optimizing the model.
discount_factor – in [0, 1].
estimation_step – the number of steps to look ahead
target_update_freq – the target network update frequency.
eval_eps – the epsilon-greedy noise added in evaluation.
unlikely_action_threshold – the threshold (tau) for unlikely actions, as shown in Equ. (17) in the paper.
imitation_logits_penalty – regularization weight for imitation logits.
estimation_step – the number of steps to look ahead.
target_update_freq – the target network update frequency (0 if you do not use the target network).
reward_normalization – normalize the returns to Normal(0, 1). TODO: rename to return_normalization?
is_double – use double dqn.
clip_loss_grad – clip the gradient of the loss in accordance with nature14236; this amounts to using the Huber loss instead of the MSE loss.
observation_space – Env’s observation space.
lr_scheduler – if not None, will be called in policy.update().
See also
Please refer to
BasePolicy
for more detailed explanation.- forward(batch: ObsBatchProtocol, state: dict | Batch | ndarray | None = None, **kwargs: Any) ImitationBatchProtocol [source]#
Compute action over the given batch data.
If you need to mask the action, please add a “mask” into batch.obs, for example, if we have an environment that has “0/1/2” three actions:
batch == Batch( obs=Batch( obs="original obs, with batch_size=1 for demonstration", mask=np.array([[False, True, False]]), # action 1 is available # action 0 and 2 are unavailable ), ... )
- Returns:
A
Batch
which has 3 keys:act
the action.logits
the network’s raw output.state
the hidden state.
See also
Please refer to
forward()
for more detailed explanation.
- learn(batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) TDiscreteBCQTrainingStats [source]#
Update policy with a given batch of data.
- Returns:
A dataclass object, including the data needed to be logged (e.g., loss).
Note
In order to distinguish the collecting state, updating state and testing state, you can check the policy state by
self.training
andself.updating
. Please refer to States for policy for more detailed explanation.Warning
If you use
torch.distributions.Normal
andtorch.distributions.Categorical
to calculate the log_prob, please be careful about the shape: Categorical distribution gives “[batch_size]” shape while Normal distribution gives “[batch_size, 1]” shape. The auto-broadcasting of numerical operation with torch tensors will amplify this error.