from typing import Any, Dict, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import Batch, ReplayBuffer, to_numpy, to_torch
from tianshou.policy import BasePolicy
from tianshou.utils.net.discrete import IntrinsicCuriosityModule
[docs]class ICMPolicy(BasePolicy):
"""Implementation of Intrinsic Curiosity Module. arXiv:1705.05363.
:param BasePolicy policy: a base policy to add ICM to.
:param IntrinsicCuriosityModule model: the ICM model.
:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
:param float lr_scale: the scaling factor for ICM learning.
:param float forward_loss_weight: the weight for forward model loss.
:param lr_scheduler: a learning rate scheduler that adjusts the learning rate in
optimizer in each policy.update(). Default to None (no lr_scheduler).
.. seealso::
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
explanation.
"""
def __init__(
self,
policy: BasePolicy,
model: IntrinsicCuriosityModule,
optim: torch.optim.Optimizer,
lr_scale: float,
reward_scale: float,
forward_loss_weight: float,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.policy = policy
self.model = model
self.optim = optim
self.lr_scale = lr_scale
self.reward_scale = reward_scale
self.forward_loss_weight = forward_loss_weight
[docs] def train(self, mode: bool = True) -> "ICMPolicy":
"""Set the module in training mode."""
self.policy.train(mode)
self.training = mode
self.model.train(mode)
return self
[docs] def forward(
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
**kwargs: Any,
) -> Batch:
"""Compute action over the given batch data by inner policy.
.. seealso::
Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
more detailed explanation.
"""
return self.policy.forward(batch, state, **kwargs)
[docs] def exploration_noise(self, act: Union[np.ndarray, Batch],
batch: Batch) -> Union[np.ndarray, Batch]:
return self.policy.exploration_noise(act, batch)
[docs] def set_eps(self, eps: float) -> None:
"""Set the eps for epsilon-greedy exploration."""
if hasattr(self.policy, "set_eps"):
self.policy.set_eps(eps) # type: ignore
else:
raise NotImplementedError()
[docs] def process_fn(
self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray
) -> Batch:
"""Pre-process the data from the provided replay buffer.
Used in :meth:`update`. Check out :ref:`process_fn` for more information.
"""
mse_loss, act_hat = self.model(batch.obs, batch.act, batch.obs_next)
batch.policy = Batch(orig_rew=batch.rew, act_hat=act_hat, mse_loss=mse_loss)
batch.rew += to_numpy(mse_loss * self.reward_scale)
return self.policy.process_fn(batch, buffer, indices)
[docs] def post_process_fn(
self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray
) -> None:
"""Post-process the data from the provided replay buffer.
Typical usage is to update the sampling weight in prioritized
experience replay. Used in :meth:`update`.
"""
self.policy.post_process_fn(batch, buffer, indices)
batch.rew = batch.policy.orig_rew # restore original reward
[docs] def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
res = self.policy.learn(batch, **kwargs)
self.optim.zero_grad()
act_hat = batch.policy.act_hat
act = to_torch(batch.act, dtype=torch.long, device=act_hat.device)
inverse_loss = F.cross_entropy(act_hat, act).mean()
forward_loss = batch.policy.mse_loss.mean()
loss = (
(1 - self.forward_loss_weight) * inverse_loss +
self.forward_loss_weight * forward_loss
) * self.lr_scale
loss.backward()
self.optim.step()
res.update(
{
"loss/icm": loss.item(),
"loss/icm/forward": forward_loss.item(),
"loss/icm/inverse": inverse_loss.item()
}
)
return res