160 lines
6.2 KiB
Python
160 lines
6.2 KiB
Python
import torch
|
|
import numpy as np
|
|
from torch import nn
|
|
from copy import deepcopy
|
|
import torch.nn.functional as F
|
|
|
|
from tianshou.data import Batch
|
|
from tianshou.policy import PGPolicy
|
|
|
|
|
|
class PPOPolicy(PGPolicy):
|
|
r"""Implementation of Proximal Policy Optimization. arXiv:1707.06347
|
|
|
|
:param torch.nn.Module actor: the actor network following the rules in
|
|
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
|
|
:param torch.nn.Module critic: the critic network. (s -> V(s))
|
|
:param torch.optim.Optimizer optim: the optimizer for actor and critic
|
|
network.
|
|
:param torch.distributions.Distribution dist_fn: for computing the action.
|
|
:param float discount_factor: in [0, 1], defaults to 0.99.
|
|
:param float max_grad_norm: clipping gradients in back propagation,
|
|
defaults to ``None``.
|
|
:param float eps_clip: :math:`\epsilon` in :math:`L_{CLIP}` in the original
|
|
paper, defaults to 0.2.
|
|
:param float vf_coef: weight for value loss, defaults to 0.5.
|
|
:param float ent_coef: weight for entropy loss, defaults to 0.01.
|
|
:param action_range: the action range (minimum, maximum).
|
|
:type action_range: [float, float]
|
|
:param float gae_lambda: in [0, 1], param for Generalized Advantage
|
|
Estimation, defaults to 0.95.
|
|
|
|
.. seealso::
|
|
|
|
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
|
|
explanation.
|
|
"""
|
|
|
|
def __init__(self, actor, critic, optim, dist_fn,
|
|
discount_factor=0.99,
|
|
max_grad_norm=.5,
|
|
eps_clip=.2,
|
|
vf_coef=.5,
|
|
ent_coef=.0,
|
|
action_range=None,
|
|
gae_lambda=0.95,
|
|
**kwargs):
|
|
super().__init__(None, None, dist_fn, discount_factor, **kwargs)
|
|
self._max_grad_norm = max_grad_norm
|
|
self._eps_clip = eps_clip
|
|
self._w_vf = vf_coef
|
|
self._w_ent = ent_coef
|
|
self._range = action_range
|
|
self.actor, self.actor_old = actor, deepcopy(actor)
|
|
self.actor_old.eval()
|
|
self.critic, self.critic_old = critic, deepcopy(critic)
|
|
self.critic_old.eval()
|
|
self.optim = optim
|
|
self._batch = 64
|
|
assert 0 <= gae_lambda <= 1, 'GAE lambda should be in [0, 1].'
|
|
self._lambda = gae_lambda
|
|
|
|
def train(self):
|
|
"""Set the module in training mode, except for the target network."""
|
|
self.training = True
|
|
self.actor.train()
|
|
self.critic.train()
|
|
|
|
def eval(self):
|
|
"""Set the module in evaluation mode, except for the target network."""
|
|
self.training = False
|
|
self.actor.eval()
|
|
self.critic.eval()
|
|
|
|
def process_fn(self, batch, buffer, indice):
|
|
if self._lambda in [0, 1]:
|
|
return self.compute_episodic_return(
|
|
batch, None, gamma=self._gamma, gae_lambda=self._lambda)
|
|
v_ = []
|
|
with torch.no_grad():
|
|
for b in batch.split(self._batch * 4, permute=False):
|
|
v_.append(self.critic(b.obs_next).detach().cpu().numpy())
|
|
v_ = np.concatenate(v_, axis=0)
|
|
batch.v_ = v_
|
|
return self.compute_episodic_return(
|
|
batch, v_, gamma=self._gamma, gae_lambda=self._lambda)
|
|
|
|
def forward(self, batch, state=None, model='actor', **kwargs):
|
|
"""Compute action over the given batch data.
|
|
|
|
:return: A :class:`~tianshou.data.Batch` 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.
|
|
"""
|
|
model = getattr(self, model)
|
|
logits, h = model(batch.obs, state=state, info=batch.info)
|
|
if isinstance(logits, tuple):
|
|
dist = self.dist_fn(*logits)
|
|
else:
|
|
dist = self.dist_fn(logits)
|
|
act = dist.sample()
|
|
if self._range:
|
|
act = act.clamp(self._range[0], self._range[1])
|
|
return Batch(logits=logits, act=act, state=h, dist=dist)
|
|
|
|
def sync_weight(self):
|
|
"""Synchronize the weight for the target network."""
|
|
self.actor_old.load_state_dict(self.actor.state_dict())
|
|
self.critic_old.load_state_dict(self.critic.state_dict())
|
|
|
|
def learn(self, batch, batch_size=None, repeat=1, **kwargs):
|
|
self._batch = batch_size
|
|
losses, clip_losses, vf_losses, ent_losses = [], [], [], []
|
|
r = batch.returns
|
|
batch.returns = (r - r.mean()) / (r.std() + self._eps)
|
|
batch.act = torch.tensor(batch.act)
|
|
batch.returns = torch.tensor(batch.returns)[:, None]
|
|
batch.v_ = torch.tensor(batch.v_)
|
|
for _ in range(repeat):
|
|
for b in batch.split(batch_size):
|
|
vs_old = self.critic_old(b.obs)
|
|
vs__old = b.v_.to(vs_old.device)
|
|
dist = self(b).dist
|
|
dist_old = self(b, model='actor_old').dist
|
|
target_v = b.returns.to(vs_old.device) + self._gamma * vs__old
|
|
adv = (target_v - vs_old).detach()
|
|
a = b.act.to(adv.device)
|
|
ratio = torch.exp(dist.log_prob(a) - dist_old.log_prob(a))
|
|
surr1 = ratio * adv
|
|
surr2 = ratio.clamp(
|
|
1. - self._eps_clip, 1. + self._eps_clip) * adv
|
|
clip_loss = -torch.min(surr1, surr2).mean()
|
|
clip_losses.append(clip_loss.item())
|
|
vf_loss = F.smooth_l1_loss(self.critic(b.obs), target_v)
|
|
vf_losses.append(vf_loss.item())
|
|
e_loss = dist.entropy().mean()
|
|
ent_losses.append(e_loss.item())
|
|
loss = clip_loss + self._w_vf * vf_loss - self._w_ent * e_loss
|
|
losses.append(loss.item())
|
|
self.optim.zero_grad()
|
|
loss.backward()
|
|
nn.utils.clip_grad_norm_(list(
|
|
self.actor.parameters()) + list(self.critic.parameters()),
|
|
self._max_grad_norm)
|
|
self.optim.step()
|
|
self.sync_weight()
|
|
return {
|
|
'loss': losses,
|
|
'loss/clip': clip_losses,
|
|
'loss/vf': vf_losses,
|
|
'loss/ent': ent_losses,
|
|
}
|