mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-03 11:00:57 +08:00
188 lines
7.4 KiB
Python
188 lines
7.4 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 Dict, List, Tuple, Union, Optional
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from tianshou.policy import PGPolicy
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from tianshou.data import Batch, ReplayBuffer
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from tianshou.data import to_torch
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from numba import njit
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import sys
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sys.path.append("..")
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from util import to_numpy, to_torch_as
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from .ppo import _episodic_return
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class PPO_sup(PGPolicy):
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"""The PPO policy with a log-likelihood supervision loss"""
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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: 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 = 0.2,
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vf_clip_para=10.0,
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vf_coef: float = 0.5,
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kl_coef=0.5,
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kl_target=0.01,
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ent_coef: float = 0.01,
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sup_coef=0.1,
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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
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) -> 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._vf_clip_para = vf_clip_para
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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.sup_coef = sup_coef
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self.kl_target = kl_target
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self.kl_coef = kl_coef
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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, "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, 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(batch, None, gamma=self._gamma, gae_lambda=self._lambda)
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else:
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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(batch, v_, gamma=self._gamma, gae_lambda=self._lambda)
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def forward(self, batch: Batch, state: Optional[Union[dict, Batch, np.ndarray]] = None, **kwargs) -> Batch:
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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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if self.training:
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act = dist.sample()
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else:
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act = torch.argmax(logits, dim=1)
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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, **kwargs) -> Dict[str, List[float]]:
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self._batch = batch_size
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losses, clip_losses, vf_losses, ent_losses, kl_losses, supervision_losses = (
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[],
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[],
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[],
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[],
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[],
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[],
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)
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v = []
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old_log_prob = []
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teacher_action = []
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old_logits = []
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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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b_ = self(b)
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dist = b_.dist
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logits = b_.logits
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old_log_prob.append(dist.log_prob(to_torch_as(b.act, v[0])))
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old_logits.append(logits)
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teacher_action.append(self.actor.teacher_action)
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batch.teacher_action = torch.cat(teacher_action, dim=0).to(torch.long)
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batch.old_logits = torch.cat(old_logits, dim=0)
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batch.v = torch.cat(v, dim=0) # old value
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batch.act = to_torch_as(batch.act, v[0])
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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, v[0]).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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res = self(b)
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logits = res.logits
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dist = res.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(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(torch.min(surr1, surr2), 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(-self._vf_clip_para, self._vf_clip_para)
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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 = torch.max(vf1, vf2).mean()
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else:
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vf_loss = (b.returns - value).pow(2).mean()
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supervision_loss = F.nll_loss(logits.log(), b.teacher_action)
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supervision_losses.append(supervision_loss.item())
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kl = torch.distributions.kl.kl_divergence(self.dist_fn(b.old_logits), dist)
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kl_loss = kl.mean()
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kl_losses.append(kl_loss.item())
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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 + self.kl_coef * kl_loss
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loss += self.sup_coef * supervision_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_(
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list(self.actor.parameters()) + list(self.critic.parameters()), self._max_grad_norm,
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)
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self.optim.step()
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if hasattr(self.actor, "callback"):
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self.actor.callback()
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cur_kl = np.mean(kl_losses)
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if cur_kl > 2.0 * self.kl_target:
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self.kl_coef *= 1.5
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elif cur_kl < 0.5 * self.kl_target:
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self.kl_coef *= 0.5
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res = {
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"loss/total_loss": losses,
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"loss/policy": clip_losses,
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"loss/vf": vf_losses,
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"loss/entropy": ent_losses,
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"loss/kl": kl_losses,
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"loss/supervision": supervision_losses,
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}
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return res
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