111 lines
4.2 KiB
Python
111 lines
4.2 KiB
Python
import torch
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import numpy as np
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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 BasePolicy
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# from tianshou.exploration import OUNoise
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class DDPGPolicy(BasePolicy):
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"""docstring for DDPGPolicy"""
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def __init__(self, actor, actor_optim, critic, critic_optim,
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tau=0.005, gamma=0.99, exploration_noise=0.1,
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action_range=None, reward_normalization=False,
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ignore_done=False):
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super().__init__()
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if actor is not None:
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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.actor_optim = actor_optim
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if critic is not None:
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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.critic_optim = critic_optim
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assert 0 < tau <= 1, 'tau should in (0, 1]'
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self._tau = tau
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assert 0 < gamma <= 1, 'gamma should in (0, 1]'
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self._gamma = gamma
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assert 0 <= exploration_noise, 'noise should not be negative'
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self._eps = exploration_noise
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assert action_range is not None
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self._range = action_range
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self._action_bias = (action_range[0] + action_range[1]) / 2
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self._action_scale = (action_range[1] - action_range[0]) / 2
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# it is only a little difference to use rand_normal
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# self.noise = OUNoise()
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self._rm_done = ignore_done
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self._rew_norm = reward_normalization
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self.__eps = np.finfo(np.float32).eps.item()
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def set_eps(self, eps):
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self._eps = eps
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def train(self):
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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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self.training = False
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self.actor.eval()
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self.critic.eval()
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def sync_weight(self):
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for o, n in zip(self.actor_old.parameters(), self.actor.parameters()):
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o.data.copy_(o.data * (1 - self._tau) + n.data * self._tau)
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for o, n in zip(
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self.critic_old.parameters(), self.critic.parameters()):
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o.data.copy_(o.data * (1 - self._tau) + n.data * self._tau)
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def process_fn(self, batch, buffer, indice):
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if self._rew_norm:
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bfr = buffer.rew[:len(buffer)]
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mean, std = bfr.mean(), bfr.std()
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if std > self.__eps:
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batch.rew = (batch.rew - mean) / std
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if self._rm_done:
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batch.done = batch.done * 0.
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return batch
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def __call__(self, batch, state=None,
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model='actor', input='obs', eps=None):
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model = getattr(self, model)
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obs = getattr(batch, input)
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logits, h = model(obs, state=state, info=batch.info)
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logits += self._action_bias
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if eps is None:
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eps = self._eps
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if eps > 0:
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# noise = np.random.normal(0, eps, size=logits.shape)
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# logits += torch.tensor(noise, device=logits.device)
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# noise = self.noise(logits.shape, eps)
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logits += torch.randn(
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size=logits.shape, device=logits.device) * eps
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logits = logits.clamp(self._range[0], self._range[1])
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return Batch(act=logits, state=h)
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def learn(self, batch, batch_size=None, repeat=1):
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target_q = self.critic_old(batch.obs_next, self(
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batch, model='actor_old', input='obs_next', eps=0).act)
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dev = target_q.device
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rew = torch.tensor(batch.rew, dtype=torch.float, device=dev)[:, None]
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done = torch.tensor(batch.done, dtype=torch.float, device=dev)[:, None]
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target_q = (rew + (1. - done) * self._gamma * target_q).detach()
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current_q = self.critic(batch.obs, batch.act)
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critic_loss = F.mse_loss(current_q, target_q)
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self.critic_optim.zero_grad()
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critic_loss.backward()
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self.critic_optim.step()
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actor_loss = -self.critic(batch.obs, self(batch, eps=0).act).mean()
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self.actor_optim.zero_grad()
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actor_loss.backward()
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self.actor_optim.step()
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self.sync_weight()
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return {
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'loss/actor': actor_loss.detach().cpu().numpy(),
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'loss/critic': critic_loss.detach().cpu().numpy(),
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}
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