import torch
import torch.nn.functional as F
from tianshou.data import Batch
from tianshou.policy import BasePolicy
[docs]class ImitationPolicy(BasePolicy):
"""Implementation of vanilla imitation learning (for continuous action space).
: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 str mode: indicate the imitation type ("continuous" or "discrete"
action space), defaults to "continuous".
.. seealso::
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
explanation.
"""
def __init__(self, model, optim, mode='continuous'):
super().__init__()
self.model = model
self.optim = optim
assert mode in ['continuous', 'discrete'], \
f'Mode {mode} is not in ["continuous", "discrete"]'
self.mode = mode
[docs] def forward(self, batch, state=None):
logits, h = self.model(batch.obs, state=state, info=batch.info)
if self.mode == 'discrete':
a = logits.max(dim=1)[1]
else:
a = logits
return Batch(logits=logits, act=a, state=h)
[docs] def learn(self, batch, **kwargs):
self.optim.zero_grad()
if self.mode == 'continuous':
a = self(batch).act
a_ = torch.tensor(batch.act, dtype=torch.float, device=a.device)
loss = F.mse_loss(a, a_)
elif self.mode == 'discrete': # classification
a = self(batch).logits
a_ = torch.tensor(batch.act, dtype=torch.long, device=a.device)
loss = F.nll_loss(a, a_)
loss.backward()
self.optim.step()
return {'loss': loss.item()}