161 lines
7.7 KiB
Python
161 lines
7.7 KiB
Python
import torch
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import numpy as np
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from torch import nn
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from typing import Any, Dict, List, Type, Optional
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from tianshou.policy import A2CPolicy
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from tianshou.data import Batch, ReplayBuffer, to_numpy, to_torch_as
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class PPOPolicy(A2CPolicy):
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r"""Implementation of Proximal Policy Optimization. arXiv:1707.06347.
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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 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 eps_clip: :math:`\epsilon` in :math:`L_{CLIP}` in the original
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paper. Default to 0.2.
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:param float dual_clip: a parameter c mentioned in arXiv:1912.09729 Equ. 5,
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where c > 1 is a constant indicating the lower bound.
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Default to 5.0 (set None if you do not want to use it).
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:param bool value_clip: a parameter mentioned in arXiv:1811.02553 Sec. 4.1.
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Default to True.
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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. Default to
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None.
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:param float gae_lambda: in [0, 1], param for Generalized Advantage Estimation.
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Default to 0.95.
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:param bool reward_normalization: normalize estimated values to have std close
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to 1, also normalize the advantage to Normal(0, 1). 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 model;
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should be as large as possible within the memory constraint. 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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eps_clip: float = 0.2,
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dual_clip: Optional[float] = None,
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value_clip: bool = True,
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**kwargs: Any,
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) -> None:
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super().__init__(actor, critic, optim, dist_fn, **kwargs)
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self._eps_clip = eps_clip
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assert dual_clip is None or dual_clip > 1.0, \
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"Dual-clip PPO parameter should greater than 1.0."
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self._dual_clip = dual_clip
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self._value_clip = value_clip
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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, v_s_, old_log_prob = [], [], []
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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(self.critic(b.obs))
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v_s_.append(self.critic(b.obs_next))
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old_log_prob.append(self(b).dist.log_prob(to_torch_as(b.act, v_s[0])))
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batch.v_s = torch.cat(v_s, dim=0).flatten() # old value
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v_s = to_numpy(batch.v_s)
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v_s_ = to_numpy(torch.cat(v_s_, dim=0).flatten())
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# when normalizing values, we do not minus self.ret_rms.mean to be numerically
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# consistent with OPENAI baselines' value normalization pipeline. Emperical
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# study also shows that "minus mean" will harm performances a tiny little bit
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# due to unknown reasons (on Mujoco envs, not confident, though).
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if self._rew_norm: # unnormalize v_s & v_s_
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v_s = v_s * np.sqrt(self.ret_rms.var + self._eps)
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v_s_ = v_s_ * np.sqrt(self.ret_rms.var + self._eps)
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unnormalized_returns, advantages = 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 / \
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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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mean, std = np.mean(advantages), np.std(advantages)
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advantages = (advantages - mean) / std
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else:
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batch.returns = unnormalized_returns
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batch.act = to_torch_as(batch.act, batch.v_s)
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batch.logp_old = torch.cat(old_log_prob, dim=0)
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batch.returns = to_torch_as(batch.returns, batch.v_s)
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batch.adv = to_torch_as(advantages, batch.v_s)
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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, clip_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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# calculate loss for actor
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dist = self(b).dist
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ratio = (dist.log_prob(b.act) - b.logp_old).exp().float()
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ratio = ratio.reshape(ratio.size(0), -1).transpose(0, 1)
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surr1 = ratio * b.adv
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surr2 = ratio.clamp(1.0 - self._eps_clip, 1.0 + self._eps_clip) * b.adv
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if self._dual_clip:
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clip_loss = -torch.max(
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torch.min(surr1, surr2), self._dual_clip * b.adv
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).mean()
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else:
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clip_loss = -torch.min(surr1, surr2).mean()
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# calculate loss for critic
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value = self.critic(b.obs).flatten()
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if self._value_clip:
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v_clip = b.v_s + (value - b.v_s).clamp(
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-self._eps_clip, self._eps_clip)
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vf1 = (b.returns - value).pow(2)
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vf2 = (b.returns - v_clip).pow(2)
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vf_loss = 0.5 * torch.max(vf1, vf2).mean()
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else:
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vf_loss = 0.5 * (b.returns - value).pow(2).mean()
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# calculate regularization and overall loss
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ent_loss = dist.entropy().mean()
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loss = clip_loss + self._weight_vf * vf_loss \
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- self._weight_ent * ent_loss
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self.optim.zero_grad()
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loss.backward()
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if self._grad_norm: # clip large gradient
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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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self.optim.step()
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clip_losses.append(clip_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/clip": clip_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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