8af7196a9a
* Enable to convert Batch data back to torch. * Add torch converter to collector. * Fix * Move to_numpy/to_torch convert in dedicated utils.py. * Use to_numpy/to_torch to convert arrays. * fix lint * fix * Add unit test to check Batch from/to numpy. * Fix Batch over Batch. Co-authored-by: Alexis Duburcq <alexis.duburcq@wandercraft.eu>
188 lines
7.9 KiB
Python
188 lines
7.9 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 Dict, List, Tuple, Union, Optional
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from tianshou.policy import PGPolicy
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from tianshou.data import Batch, ReplayBuffer, to_numpy, to_torch
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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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:param float gae_lambda: in [0, 1], param for Generalized Advantage
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Estimation, defaults to 0.95.
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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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defaults 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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defaults to ``True``.
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:param bool reward_normalization: normalize the returns to Normal(0, 1),
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defaults to ``True``.
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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__(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: torch.distributions.Distribution,
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discount_factor: float = 0.99,
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max_grad_norm: Optional[float] = None,
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eps_clip: float = .2,
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vf_coef: float = .5,
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ent_coef: float = .01,
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action_range: Optional[Tuple[float, float]] = None,
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gae_lambda: float = 0.95,
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dual_clip: Optional[float] = None,
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value_clip: bool = True,
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reward_normalization: bool = True,
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**kwargs) -> None:
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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 = actor
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self.critic = critic
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self.optim = optim
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self._batch = 64
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assert 0 <= gae_lambda <= 1, 'GAE lambda should be in [0, 1].'
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self._lambda = gae_lambda
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assert dual_clip is None or dual_clip > 1, \
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'Dual-clip PPO parameter should greater than 1.'
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self._dual_clip = dual_clip
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self._value_clip = value_clip
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self._rew_norm = reward_normalization
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def process_fn(self, batch: Batch, buffer: ReplayBuffer,
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indice: np.ndarray) -> Batch:
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if self._rew_norm:
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mean, std = batch.rew.mean(), batch.rew.std()
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if not np.isclose(std, 0):
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batch.rew = (batch.rew - mean) / std
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if self._lambda in [0, 1]:
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return self.compute_episodic_return(
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batch, None, gamma=self._gamma, gae_lambda=self._lambda)
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v_ = []
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with torch.no_grad():
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for b in batch.split(self._batch, shuffle=False):
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v_.append(self.critic(b.obs_next))
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v_ = to_numpy(torch.cat(v_, dim=0))
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return self.compute_episodic_return(
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batch, v_, gamma=self._gamma, gae_lambda=self._lambda)
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def forward(self, batch: Batch,
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state: Optional[Union[dict, Batch, np.ndarray]] = None,
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**kwargs) -> 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.actor(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 learn(self, batch: Batch, batch_size: int, repeat: int,
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**kwargs) -> Dict[str, List[float]]:
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self._batch = batch_size
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losses, clip_losses, vf_losses, ent_losses = [], [], [], []
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v = []
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old_log_prob = []
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with torch.no_grad():
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for b in batch.split(batch_size, shuffle=False):
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v.append(self.critic(b.obs))
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old_log_prob.append(self(b).dist.log_prob(
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to_torch(b.act, device=v[0].device)))
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batch.v = torch.cat(v, dim=0) # old value
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dev = batch.v.device
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batch.act = to_torch(batch.act, dtype=torch.float, device=dev)
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batch.logp_old = torch.cat(old_log_prob, dim=0)
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batch.returns = to_torch(
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batch.returns, dtype=torch.float, device=dev
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).reshape(batch.v.shape)
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if self._rew_norm:
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mean, std = batch.returns.mean(), batch.returns.std()
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if not np.isclose(std.item(), 0):
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batch.returns = (batch.returns - mean) / std
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batch.adv = batch.returns - batch.v
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if self._rew_norm:
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mean, std = batch.adv.mean(), batch.adv.std()
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if not np.isclose(std.item(), 0):
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batch.adv = (batch.adv - mean) / std
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for _ in range(repeat):
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for b in batch.split(batch_size):
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dist = self(b).dist
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value = self.critic(b.obs)
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ratio = (dist.log_prob(b.act) - b.logp_old).exp().float()
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surr1 = ratio * b.adv
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surr2 = ratio.clamp(
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1. - self._eps_clip, 1. + self._eps_clip) * b.adv
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if self._dual_clip:
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clip_loss = -torch.max(torch.min(surr1, surr2),
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self._dual_clip * b.adv).mean()
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else:
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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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if self._value_clip:
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v_clip = b.v + (value - b.v).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 = .5 * torch.max(vf1, vf2).mean()
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else:
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vf_loss = .5 * (b.returns - value).pow(2).mean()
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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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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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