132 lines
5.2 KiB
Python
132 lines
5.2 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 copy import deepcopy
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import torch.nn.functional as F
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from tianshou.data import Batch
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from tianshou.policy import PGPolicy
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class PPOPolicy(PGPolicy):
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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
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network.
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:param torch.distributions.Distribution dist_fn: for computing the action.
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:param float discount_factor: in [0, 1], defaults to 0.99.
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:param float max_grad_norm: clipping gradients in back propagation,
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defaults to ``None``.
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:param float eps_clip: :math:`\epsilon` in :math:`L_{CLIP}` in the original
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paper, defaults to 0.2.
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:param float vf_coef: weight for value loss, defaults to 0.5.
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:param float ent_coef: weight for entropy loss, defaults to 0.01.
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:param action_range: the action range (minimum, maximum).
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:type action_range: [float, float]
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"""
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def __init__(self, actor, critic, optim, dist_fn,
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discount_factor=0.99,
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max_grad_norm=.5,
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eps_clip=.2,
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vf_coef=.5,
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ent_coef=.0,
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action_range=None,
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**kwargs):
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super().__init__(None, None, dist_fn, discount_factor, **kwargs)
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self._max_grad_norm = max_grad_norm
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self._eps_clip = eps_clip
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self._w_vf = vf_coef
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self._w_ent = ent_coef
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self._range = action_range
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self.actor, self.actor_old = actor, deepcopy(actor)
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self.actor_old.eval()
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self.critic, self.critic_old = critic, deepcopy(critic)
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self.critic_old.eval()
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self.optim = optim
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def train(self):
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"""Set the module in training mode, except for the target network."""
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self.training = True
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self.actor.train()
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self.critic.train()
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def eval(self):
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"""Set the module in evaluation mode, except for the target network."""
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self.training = False
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self.actor.eval()
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self.critic.eval()
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def __call__(self, batch, state=None, model='actor', **kwargs):
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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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More information can be found at
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:meth:`~tianshou.policy.BasePolicy.__call__`.
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"""
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model = getattr(self, model)
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logits, h = 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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if self._range:
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act = act.clamp(self._range[0], self._range[1])
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return Batch(logits=logits, act=act, state=h, dist=dist)
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def sync_weight(self):
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"""Synchronize the weight for the target network."""
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self.actor_old.load_state_dict(self.actor.state_dict())
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self.critic_old.load_state_dict(self.critic.state_dict())
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def learn(self, batch, batch_size=None, repeat=1, **kwargs):
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losses, clip_losses, vf_losses, ent_losses = [], [], [], []
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r = batch.returns
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batch.returns = (r - r.mean()) / (r.std() + self._eps)
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batch.act = torch.tensor(batch.act)
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batch.returns = torch.tensor(batch.returns)[:, None]
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for _ in range(repeat):
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for b in batch.split(batch_size):
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vs_old, vs__old = self.critic_old(np.concatenate([
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b.obs, b.obs_next])).split(b.obs.shape[0])
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dist = self(b).dist
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dist_old = self(b, model='actor_old').dist
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target_v = b.returns.to(vs__old.device) + self._gamma * vs__old
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adv = (target_v - vs_old).detach()
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a = b.act.to(adv.device)
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ratio = torch.exp(dist.log_prob(a) - dist_old.log_prob(a))
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surr1 = ratio * adv
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surr2 = ratio.clamp(
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1. - self._eps_clip, 1. + self._eps_clip) * adv
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clip_loss = -torch.min(surr1, surr2).mean()
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clip_losses.append(clip_loss.item())
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vf_loss = F.smooth_l1_loss(self.critic(b.obs), target_v)
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vf_losses.append(vf_loss.item())
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e_loss = dist.entropy().mean()
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ent_losses.append(e_loss.item())
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loss = clip_loss + self._w_vf * vf_loss - self._w_ent * e_loss
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losses.append(loss.item())
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self.optim.zero_grad()
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loss.backward()
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nn.utils.clip_grad_norm_(list(
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self.actor.parameters()) + list(self.critic.parameters()),
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self._max_grad_norm)
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self.optim.step()
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self.sync_weight()
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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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