146 lines
5.6 KiB
Python
146 lines
5.6 KiB
Python
import torch
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import numpy as np
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from typing import Any, Dict, List, Type, Union, Optional
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from tianshou.policy import BasePolicy
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from tianshou.data import Batch, ReplayBuffer, to_torch_as
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class PGPolicy(BasePolicy):
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"""Implementation of Vanilla Policy Gradient.
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:param torch.nn.Module model: a model following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> logits)
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:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
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:param dist_fn: distribution class for computing the action.
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:type dist_fn: Type[torch.distributions.Distribution]
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:param float discount_factor: in [0, 1]. Default to 0.99.
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:param bool action_scaling: whether to map actions from range [-1, 1] to range
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[action_spaces.low, action_spaces.high]. Default to True.
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:param str action_bound_method: method to bound action to range [-1, 1], can be
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either "clip" (for simply clipping the action), "tanh" (for applying tanh
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squashing) for now, or empty string for no bounding. Default to "clip".
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:param Optional[gym.Space] action_space: env's action space, mandatory if you want
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to use option "action_scaling" or "action_bound_method". Default to None.
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:param lr_scheduler: a learning rate scheduler that adjusts the learning rate in
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optimizer in each policy.update(). Default to None (no lr_scheduler).
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.. seealso::
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Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
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explanation.
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"""
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def __init__(
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self,
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model: Optional[torch.nn.Module],
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optim: torch.optim.Optimizer,
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dist_fn: Type[torch.distributions.Distribution],
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discount_factor: float = 0.99,
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reward_normalization: bool = False,
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action_scaling: bool = True,
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action_bound_method: str = "clip",
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lr_scheduler: Optional[torch.optim.lr_scheduler.LambdaLR] = None,
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**kwargs: Any,
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) -> None:
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super().__init__(action_scaling=action_scaling,
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action_bound_method=action_bound_method, **kwargs)
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if model is not None:
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self.model: torch.nn.Module = model
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self.optim = optim
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self.lr_scheduler = lr_scheduler
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self.dist_fn = dist_fn
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assert 0.0 <= discount_factor <= 1.0, "discount factor should be in [0, 1]"
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self._gamma = discount_factor
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self._rew_norm = reward_normalization
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def process_fn(
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self, batch: Batch, buffer: ReplayBuffer, indice: np.ndarray
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) -> Batch:
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r"""Compute the discounted returns for each transition.
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.. math::
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G_t = \sum_{i=t}^T \gamma^{i-t}r_i
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where :math:`T` is the terminal time step, :math:`\gamma` is the
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discount factor, :math:`\gamma \in [0, 1]`.
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"""
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# batch.returns = self._vanilla_returns(batch)
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# batch.returns = self._vectorized_returns(batch)
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return self.compute_episodic_return(
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batch, buffer, indice, gamma=self._gamma,
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gae_lambda=1.0, rew_norm=self._rew_norm)
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def forward(
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self,
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batch: Batch,
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state: Optional[Union[dict, Batch, np.ndarray]] = None,
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**kwargs: Any,
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) -> Batch:
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"""Compute action over the given batch data.
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:return: A :class:`~tianshou.data.Batch` which has 4 keys:
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* ``act`` the action.
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* ``logits`` the network's raw output.
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* ``dist`` the action distribution.
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* ``state`` the hidden state.
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.. seealso::
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Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
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more detailed explanation.
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"""
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logits, h = self.model(batch.obs, state=state, info=batch.info)
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if isinstance(logits, tuple):
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dist = self.dist_fn(*logits)
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else:
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dist = self.dist_fn(logits)
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act = dist.sample()
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return Batch(logits=logits, act=act, state=h, dist=dist)
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def learn( # type: ignore
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self, batch: Batch, batch_size: int, repeat: int, **kwargs: Any
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) -> Dict[str, List[float]]:
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losses = []
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for _ in range(repeat):
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for b in batch.split(batch_size, merge_last=True):
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self.optim.zero_grad()
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dist = self(b).dist
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a = to_torch_as(b.act, dist.logits)
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r = to_torch_as(b.returns, dist.logits)
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log_prob = dist.log_prob(a).reshape(len(r), -1).transpose(0, 1)
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loss = -(log_prob * r).mean()
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loss.backward()
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self.optim.step()
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losses.append(loss.item())
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# update learning rate if lr_scheduler is given
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if self.lr_scheduler is not None:
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self.lr_scheduler.step()
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return {"loss": losses}
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# def _vanilla_returns(self, batch):
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# returns = batch.rew[:]
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# last = 0
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# for i in range(len(returns) - 1, -1, -1):
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# if not batch.done[i]:
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# returns[i] += self._gamma * last
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# last = returns[i]
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# return returns
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# def _vectorized_returns(self, batch):
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# # according to my tests, it is slower than _vanilla_returns
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# # import scipy.signal
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# convolve = np.convolve
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# # convolve = scipy.signal.convolve
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# rew = batch.rew[::-1]
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# batch_size = len(rew)
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# gammas = self._gamma ** np.arange(batch_size)
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# c = convolve(rew, gammas)[:batch_size]
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# T = np.where(batch.done[::-1])[0]
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# d = np.zeros_like(rew)
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# d[T] += c[T] - rew[T]
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# d[T[1:]] -= d[T[:-1]] * self._gamma ** np.diff(T)
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# return (c - convolve(d, gammas)[:batch_size])[::-1]
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