e27b5a26f3
- simplify code - apply value normalization (global) and adv norm (per-batch) in on-policy algorithms
130 lines
5.6 KiB
Python
130 lines
5.6 KiB
Python
import torch
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import numpy as np
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from torch import nn
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import torch.nn.functional as F
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from typing import Any, Dict, List, Type, Optional
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from tianshou.policy import PGPolicy
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from tianshou.data import Batch, ReplayBuffer, to_torch_as, to_numpy
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class A2CPolicy(PGPolicy):
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"""Implementation of Synchronous Advantage Actor-Critic. arXiv:1602.01783.
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:param torch.nn.Module actor: the actor network following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> logits)
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:param torch.nn.Module critic: the critic network. (s -> V(s))
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:param torch.optim.Optimizer optim: the optimizer for actor and critic
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network.
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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 float vf_coef: weight for value loss. Default to 0.5.
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:param float ent_coef: weight for entropy loss. Default to 0.01.
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:param float max_grad_norm: clipping gradients in back propagation.
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Default to None.
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:param float gae_lambda: in [0, 1], param for Generalized Advantage
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Estimation. Default to 0.95.
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:param bool reward_normalization: normalize the reward to Normal(0, 1).
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Default to False.
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:param int max_batchsize: the maximum size of the batch when computing GAE,
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depends on the size of available memory and the memory cost of the
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model; should be as large as possible within the memory constraint.
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Default to 256.
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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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actor: torch.nn.Module,
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critic: 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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vf_coef: float = 0.5,
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ent_coef: float = 0.01,
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max_grad_norm: Optional[float] = None,
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gae_lambda: float = 0.95,
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max_batchsize: int = 256,
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**kwargs: Any
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) -> None:
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super().__init__(actor, optim, dist_fn, **kwargs)
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self.critic = critic
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assert 0.0 <= gae_lambda <= 1.0, "GAE lambda should be in [0, 1]."
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self._lambda = gae_lambda
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self._weight_vf = vf_coef
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self._weight_ent = ent_coef
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self._grad_norm = max_grad_norm
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self._batch = max_batchsize
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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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v_s_ = []
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with torch.no_grad():
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for b in batch.split(self._batch, shuffle=False, merge_last=True):
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v_s_.append(to_numpy(self.critic(b.obs_next)))
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v_s_ = np.concatenate(v_s_, axis=0)
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if self._rew_norm: # unnormalize v_s_
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v_s_ = v_s_ * np.sqrt(self.ret_rms.var + self._eps) + self.ret_rms.mean
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unnormalized_returns, _ = self.compute_episodic_return(
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batch, buffer, indice, v_s_=v_s_,
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gamma=self._gamma, gae_lambda=self._lambda)
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if self._rew_norm:
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batch.returns = (unnormalized_returns - self.ret_rms.mean) / \
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np.sqrt(self.ret_rms.var + self._eps)
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self.ret_rms.update(unnormalized_returns)
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else:
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batch.returns = unnormalized_returns
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return batch
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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, actor_losses, vf_losses, ent_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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v = self.critic(b.obs).flatten()
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a = to_torch_as(b.act, v)
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r = to_torch_as(b.returns, v)
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log_prob = dist.log_prob(a).reshape(len(r), -1).transpose(0, 1)
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a_loss = -(log_prob * (r - v).detach()).mean()
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vf_loss = F.mse_loss(r, v) # type: ignore
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ent_loss = dist.entropy().mean()
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loss = a_loss + self._weight_vf * vf_loss - self._weight_ent * ent_loss
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loss.backward()
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if self._grad_norm is not None:
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nn.utils.clip_grad_norm_(
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list(self.actor.parameters()) + list(self.critic.parameters()),
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max_norm=self._grad_norm,
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)
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self.optim.step()
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actor_losses.append(a_loss.item())
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vf_losses.append(vf_loss.item())
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ent_losses.append(ent_loss.item())
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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 {
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"loss": losses,
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"loss/actor": actor_losses,
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"loss/vf": vf_losses,
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"loss/ent": ent_losses,
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}
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