Source code for

from typing import Any, Dict, List, Optional, Type, Union

import numpy as np
import torch

from import Batch, ReplayBuffer, to_torch, to_torch_as
from tianshou.policy import BasePolicy
from tianshou.utils import RunningMeanStd

[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", deterministic_eval: bool = False, **kwargs: Any, ) -> None: super().__init__( action_scaling=action_scaling, action_bound_method=action_bound_method, **kwargs ) = model self.optim = optim 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, indices: 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(indices.shape, self.ret_rms.mean) unnormalized_returns, _ = self.compute_episodic_return( batch, buffer, indices, 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:`` 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, hidden =, state=state, if isinstance(logits, tuple): dist = self.dist_fn(*logits) else: dist = self.dist_fn(logits) if self._deterministic_eval and not 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=hidden, 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 minibatch in batch.split(batch_size, merge_last=True): self.optim.zero_grad() result = self(minibatch) dist = result.dist act = to_torch_as(minibatch.act, result.act) ret = to_torch(minibatch.returns, torch.float, result.act.device) log_prob = dist.log_prob(act).reshape(len(ret), -1).transpose(0, 1) loss = -(log_prob * ret).mean() loss.backward() self.optim.step() losses.append(loss.item()) return {"loss": losses}