fc251ab0b8
* add makefile * bump version * add isort and yapf * update contributing.md * update PR template * spelling check
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
from typing import Any, Dict, Optional, Union
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import numpy as np
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import torch
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import torch.nn.functional as F
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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.
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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 gym.Space action_space: env's action space.
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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__(
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self,
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model: torch.nn.Module,
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optim: torch.optim.Optimizer,
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**kwargs: Any,
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) -> None:
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super().__init__(**kwargs)
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self.model = model
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self.optim = optim
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assert self.action_type in ["continuous", "discrete"], \
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"Please specify action_space."
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def forward(
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self,
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batch: Batch,
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state: Optional[Union[dict, Batch, np.ndarray]] = None,
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**kwargs: Any,
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) -> Batch:
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logits, h = self.model(batch.obs, state=state, info=batch.info)
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if self.action_type == "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: Any) -> Dict[str, float]:
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self.optim.zero_grad()
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if self.action_type == "continuous": # regression
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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_) # type: ignore
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elif self.action_type == "discrete": # classification
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a = F.log_softmax(self(batch).logits, dim=-1)
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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_) # type: ignore
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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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