122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
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
|
|
|
|
|
|
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.
|
|
|
|
.. 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
|
|
|
|
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
|
|
|
|
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)
|
|
|
|
def exploration_noise(self, act: Union[np.ndarray, Batch],
|
|
batch: Batch) -> Union[np.ndarray, Batch]:
|
|
return self.policy.exploration_noise(act, batch)
|
|
|
|
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()
|
|
|
|
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)
|
|
|
|
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
|
|
|
|
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() # type: ignore
|
|
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
|