3a08e27ed4
* code refactor; remove unused kwargs; add reward_normalization for dqn * bugfix for __setitem__ with torch.Tensor; add Batch.condense * minor fix * support cat with empty Batch * remove the dependency of is_empty on len; specify the semantic of empty Batch by test cases * support stack with empty Batch * remove condense * refactor code to reflect the shared / partial / reserved categories of keys * add is_empty(recursive=False) * doc fix * docfix and bugfix for _is_batch_set * add doc for key reservation * bugfix for algebra operators * fix cat with lens hint * code refactor * bugfix for storing None * use ValueError instead of exception * hide lens away from users * add comment for __cat * move the computation of the initial value of lens in cat_ itself. * change the place of doc string * doc fix for Batch doc string * change recursive to recurse * doc string fix * minor fix for batch doc
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
import torch
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import numpy as np
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import torch.nn.functional as F
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from typing import Dict, Union, Optional
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from tianshou.data import Batch, to_torch
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from tianshou.policy import BasePolicy
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class ImitationPolicy(BasePolicy):
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"""Implementation of vanilla imitation learning (for continuous action space).
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:param torch.nn.Module model: a model following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> a)
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:param torch.optim.Optimizer optim: for optimizing the model.
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:param str mode: indicate the imitation type ("continuous" or "discrete"
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action space), defaults to "continuous".
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.. seealso::
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Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
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explanation.
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"""
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def __init__(self, model: torch.nn.Module, optim: torch.optim.Optimizer,
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mode: str = 'continuous') -> None:
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super().__init__()
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self.model = model
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self.optim = optim
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assert mode in ['continuous', 'discrete'], \
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f'Mode {mode} is not in ["continuous", "discrete"]'
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self.mode = mode
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def forward(self,
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batch: Batch,
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state: Optional[Union[dict, Batch, np.ndarray]] = None,
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**kwargs) -> Batch:
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logits, h = self.model(batch.obs, state=state, info=batch.info)
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if self.mode == 'discrete':
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a = logits.max(dim=1)[1]
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else:
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a = logits
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return Batch(logits=logits, act=a, state=h)
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def learn(self, batch: Batch, **kwargs) -> Dict[str, float]:
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self.optim.zero_grad()
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if self.mode == 'continuous':
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a = self(batch).act
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a_ = to_torch(batch.act, dtype=torch.float32, device=a.device)
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loss = F.mse_loss(a, a_)
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elif self.mode == 'discrete': # classification
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a = self(batch).logits
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a_ = to_torch(batch.act, dtype=torch.long, device=a.device)
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loss = F.nll_loss(a, a_)
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loss.backward()
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self.optim.step()
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return {'loss': loss.item()}
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