import torch
import numpy as np
from typing import Any, Dict, List, Type, Union, Optional
from tianshou.policy import BasePolicy
from tianshou.utils import RunningMeanStd
from tianshou.data import Batch, ReplayBuffer, to_torch_as
[docs]class PGPolicy(BasePolicy):
"""Implementation of REINFORCE algorithm.
:param torch.nn.Module model: a model following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
:param dist_fn: distribution class for computing the action.
:type dist_fn: Type[torch.distributions.Distribution]
:param float discount_factor: in [0, 1]. Default to 0.99.
:param bool action_scaling: whether to map actions from range [-1, 1] to range
[action_spaces.low, action_spaces.high]. Default to True.
:param str action_bound_method: method to bound action to range [-1, 1], can be
either "clip" (for simply clipping the action), "tanh" (for applying tanh
squashing) for now, or empty string for no bounding. Default to "clip".
:param Optional[gym.Space] action_space: env's action space, mandatory if you want
to use option "action_scaling" or "action_bound_method". Default to None.
:param lr_scheduler: a learning rate scheduler that adjusts the learning rate in
optimizer in each policy.update(). Default to None (no lr_scheduler).
:param bool deterministic_eval: whether to use deterministic action instead of
stochastic action sampled by the policy. Default to False.
.. seealso::
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
explanation.
"""
def __init__(
self,
model: torch.nn.Module,
optim: torch.optim.Optimizer,
dist_fn: Type[torch.distributions.Distribution],
discount_factor: float = 0.99,
reward_normalization: bool = False,
action_scaling: bool = True,
action_bound_method: str = "clip",
lr_scheduler: Optional[torch.optim.lr_scheduler.LambdaLR] = None,
deterministic_eval: bool = False,
**kwargs: Any,
) -> None:
super().__init__(action_scaling=action_scaling,
action_bound_method=action_bound_method, **kwargs)
self.actor = model
self.optim = optim
self.lr_scheduler = lr_scheduler
self.dist_fn = dist_fn
assert 0.0 <= discount_factor <= 1.0, "discount factor should be in [0, 1]"
self._gamma = discount_factor
self._rew_norm = reward_normalization
self.ret_rms = RunningMeanStd()
self._eps = 1e-8
self._deterministic_eval = deterministic_eval
[docs] def process_fn(
self, batch: Batch, buffer: ReplayBuffer, indice: np.ndarray
) -> Batch:
r"""Compute the discounted returns for each transition.
.. math::
G_t = \sum_{i=t}^T \gamma^{i-t}r_i
where :math:`T` is the terminal time step, :math:`\gamma` is the
discount factor, :math:`\gamma \in [0, 1]`.
"""
v_s_ = np.full(indice.shape, self.ret_rms.mean)
unnormalized_returns, _ = self.compute_episodic_return(
batch, buffer, indice, v_s_=v_s_, gamma=self._gamma, gae_lambda=1.0)
if self._rew_norm:
batch.returns = (unnormalized_returns - self.ret_rms.mean) / \
np.sqrt(self.ret_rms.var + self._eps)
self.ret_rms.update(unnormalized_returns)
else:
batch.returns = unnormalized_returns
return batch
[docs] def forward(
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
**kwargs: Any,
) -> Batch:
"""Compute action over the given batch data.
:return: A :class:`~tianshou.data.Batch` which has 4 keys:
* ``act`` the action.
* ``logits`` the network's raw output.
* ``dist`` the action distribution.
* ``state`` the hidden state.
.. seealso::
Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
more detailed explanation.
"""
logits, h = self.actor(batch.obs, state=state)
if isinstance(logits, tuple):
dist = self.dist_fn(*logits)
else:
dist = self.dist_fn(logits)
if self._deterministic_eval and not self.training:
if self.action_type == "discrete":
act = logits.argmax(-1)
elif self.action_type == "continuous":
act = logits[0]
else:
act = dist.sample()
return Batch(logits=logits, act=act, state=h, dist=dist)
[docs] def learn( # type: ignore
self, batch: Batch, batch_size: int, repeat: int, **kwargs: Any
) -> Dict[str, List[float]]:
losses = []
for _ in range(repeat):
for b in batch.split(batch_size, merge_last=True):
self.optim.zero_grad()
result = self(b)
dist = result.dist
a = to_torch_as(b.act, result.act)
ret = to_torch_as(b.returns, result.act)
log_prob = dist.log_prob(a).reshape(len(ret), -1).transpose(0, 1)
loss = -(log_prob * ret).mean()
loss.backward()
self.optim.step()
losses.append(loss.item())
# update learning rate if lr_scheduler is given
if self.lr_scheduler is not None:
self.lr_scheduler.step()
return {"loss": losses}