import torch
import numpy as np
import torch.nn.functional as F
from typing import Any, Dict, Union, Optional
from tianshou.data import Batch, to_torch
from tianshou.policy import BasePolicy
[docs]class ImitationPolicy(BasePolicy):
"""Implementation of vanilla imitation learning.
:param torch.nn.Module model: a model following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> a)
:param torch.optim.Optimizer optim: for optimizing the model.
:param gym.Space action_space: env's action space.
.. seealso::
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
explanation.
"""
def __init__(
self,
model: torch.nn.Module,
optim: torch.optim.Optimizer,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.model = model
self.optim = optim
assert self.action_type in ["continuous", "discrete"], \
"Please specify action_space."
[docs] def forward(
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
**kwargs: Any,
) -> Batch:
logits, h = self.model(batch.obs, state=state, info=batch.info)
if self.action_type == "discrete":
a = logits.max(dim=1)[1]
else:
a = logits
return Batch(logits=logits, act=a, state=h)
[docs] def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
self.optim.zero_grad()
if self.action_type == "continuous": # regression
a = self(batch).act
a_ = to_torch(batch.act, dtype=torch.float32, device=a.device)
loss = F.mse_loss(a, a_) # type: ignore
elif self.action_type == "discrete": # classification
a = F.log_softmax(self(batch).logits, dim=-1)
a_ = to_torch(batch.act, dtype=torch.long, device=a.device)
loss = F.nll_loss(a, a_) # type: ignore
loss.backward()
self.optim.step()
return {"loss": loss.item()}