94bfb32cc1
1. add policy.eval() in all test scripts' "watch performance" 2. remove dict return support for collector preprocess_fn 3. add `__contains__` and `pop` in batch: `key in batch`, `batch.pop(key, deft)` 4. exact n_episode for a list of n_episode limitation and save fake data in cache_buffer when self.buffer is None (#184) 5. fix tensorboard logging: h-axis stands for env step instead of gradient step; add test results into tensorboard 6. add test_returns (both GAE and nstep) 7. change the type-checking order in batch.py and converter.py in order to meet the most often case first 8. fix shape inconsistency for torch.Tensor in replay buffer 9. remove `**kwargs` in ReplayBuffer 10. remove default value in batch.split() and add merge_last argument (#185) 11. improve nstep efficiency 12. add max_batchsize in onpolicy algorithms 13. potential bugfix for subproc.wait 14. fix RecurrentActorProb 15. improve the code-coverage (from 90% to 95%) and remove the dead code 16. fix some incorrect type annotation The above improvement also increases the training FPS: on my computer, the previous version is only ~1800 FPS and after that, it can reach ~2050 (faster than v0.2.4.post1).
188 lines
7.8 KiB
Python
188 lines
7.8 KiB
Python
import torch
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import numpy as np
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from copy import deepcopy
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from typing import Dict, Tuple, Union, Optional
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from tianshou.policy import DDPGPolicy
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from tianshou.policy.dist import DiagGaussian
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from tianshou.data import Batch, to_torch_as, ReplayBuffer
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from tianshou.exploration import BaseNoise
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class SACPolicy(DDPGPolicy):
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"""Implementation of Soft Actor-Critic. arXiv:1812.05905
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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.optim.Optimizer actor_optim: the optimizer for actor network.
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:param torch.nn.Module critic1: the first critic network. (s, a -> Q(s,
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a))
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:param torch.optim.Optimizer critic1_optim: the optimizer for the first
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critic network.
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:param torch.nn.Module critic2: the second critic network. (s, a -> Q(s,
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a))
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:param torch.optim.Optimizer critic2_optim: the optimizer for the second
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critic network.
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:param float tau: param for soft update of the target network, defaults to
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0.005.
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:param float gamma: discount factor, in [0, 1], defaults to 0.99.
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:param float exploration_noise: the noise intensity, add to the action,
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defaults to 0.1.
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:param (float, torch.Tensor, torch.optim.Optimizer) or float alpha: entropy
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regularization coefficient, default to 0.2.
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If a tuple (target_entropy, log_alpha, alpha_optim) is provided, then
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alpha is automatatically tuned.
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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 bool reward_normalization: normalize the reward to Normal(0, 1),
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defaults to ``False``.
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:param bool ignore_done: ignore the done flag while training the policy,
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defaults to ``False``.
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:param BaseNoise exploration_noise: add a noise to action for exploration.
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This is useful when solving hard-exploration problem.
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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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actor_optim: torch.optim.Optimizer,
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critic1: torch.nn.Module,
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critic1_optim: torch.optim.Optimizer,
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critic2: torch.nn.Module,
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critic2_optim: torch.optim.Optimizer,
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tau: float = 0.005,
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gamma: float = 0.99,
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alpha: Tuple[float, torch.Tensor, torch.optim.Optimizer]
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or float = 0.2,
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action_range: Optional[Tuple[float, float]] = None,
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reward_normalization: bool = False,
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ignore_done: bool = False,
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estimation_step: int = 1,
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exploration_noise: Optional[BaseNoise] = None,
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**kwargs) -> None:
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super().__init__(None, None, None, None, tau, gamma, exploration_noise,
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action_range, reward_normalization, ignore_done,
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estimation_step, **kwargs)
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self.actor, self.actor_optim = actor, actor_optim
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self.critic1, self.critic1_old = critic1, deepcopy(critic1)
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self.critic1_old.eval()
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self.critic1_optim = critic1_optim
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self.critic2, self.critic2_old = critic2, deepcopy(critic2)
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self.critic2_old.eval()
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self.critic2_optim = critic2_optim
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self._automatic_alpha_tuning = not isinstance(alpha, float)
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if self._automatic_alpha_tuning:
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self._target_entropy = alpha[0]
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assert(alpha[1].shape == torch.Size([1])
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and alpha[1].requires_grad)
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self._log_alpha = alpha[1]
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self._alpha_optim = alpha[2]
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self._alpha = self._log_alpha.exp()
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else:
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self._alpha = alpha
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self.__eps = np.finfo(np.float32).eps.item()
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def train(self, mode=True) -> torch.nn.Module:
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self.training = mode
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self.actor.train(mode)
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self.critic1.train(mode)
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self.critic2.train(mode)
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return self
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def sync_weight(self) -> None:
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for o, n in zip(
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self.critic1_old.parameters(), self.critic1.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.critic2_old.parameters(), self.critic2.parameters()):
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o.data.copy_(o.data * (1 - self._tau) + n.data * self._tau)
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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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input: str = 'obs',
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explorating: bool = True,
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**kwargs) -> Batch:
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obs = getattr(batch, input)
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logits, h = self.actor(obs, state=state, info=batch.info)
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assert isinstance(logits, tuple)
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dist = DiagGaussian(*logits)
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x = dist.rsample()
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y = torch.tanh(x)
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act = y * self._action_scale + self._action_bias
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y = self._action_scale * (1 - y.pow(2)) + self.__eps
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log_prob = dist.log_prob(x) - torch.log(y).sum(-1, keepdim=True)
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if self._noise is not None and self.training and explorating:
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act += to_torch_as(self._noise(act.shape), act)
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act = act.clamp(self._range[0], self._range[1])
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return Batch(
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logits=logits, act=act, state=h, dist=dist, log_prob=log_prob)
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def _target_q(self, buffer: ReplayBuffer,
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indice: np.ndarray) -> torch.Tensor:
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batch = buffer[indice] # batch.obs: s_{t+n}
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with torch.no_grad():
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obs_next_result = self(batch, input='obs_next', explorating=False)
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a_ = obs_next_result.act
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batch.act = to_torch_as(batch.act, a_)
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target_q = torch.min(
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self.critic1_old(batch.obs_next, a_),
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self.critic2_old(batch.obs_next, a_),
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) - self._alpha * obs_next_result.log_prob
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return target_q
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def learn(self, batch: Batch, **kwargs) -> Dict[str, float]:
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weight = batch.pop('weight', 1.)
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# critic 1
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current_q1 = self.critic1(batch.obs, batch.act).flatten()
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target_q = batch.returns.flatten()
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td1 = current_q1 - target_q
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critic1_loss = (td1.pow(2) * weight).mean()
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# critic1_loss = F.mse_loss(current_q1, target_q)
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self.critic1_optim.zero_grad()
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critic1_loss.backward()
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self.critic1_optim.step()
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# critic 2
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current_q2 = self.critic2(batch.obs, batch.act).flatten()
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td2 = current_q2 - target_q
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critic2_loss = (td2.pow(2) * weight).mean()
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# critic2_loss = F.mse_loss(current_q2, target_q)
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self.critic2_optim.zero_grad()
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critic2_loss.backward()
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self.critic2_optim.step()
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batch.weight = (td1 + td2) / 2. # prio-buffer
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# actor
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obs_result = self(batch, explorating=False)
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a = obs_result.act
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current_q1a = self.critic1(batch.obs, a).flatten()
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current_q2a = self.critic2(batch.obs, a).flatten()
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actor_loss = (self._alpha * obs_result.log_prob.flatten()
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- torch.min(current_q1a, current_q2a)).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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if self._automatic_alpha_tuning:
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log_prob = (obs_result.log_prob + self._target_entropy).detach()
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alpha_loss = -(self._log_alpha * log_prob).mean()
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self._alpha_optim.zero_grad()
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alpha_loss.backward()
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self._alpha_optim.step()
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self._alpha = self._log_alpha.exp()
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self.sync_weight()
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result = {
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'loss/actor': actor_loss.item(),
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'loss/critic1': critic1_loss.item(),
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'loss/critic2': critic2_loss.item(),
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}
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if self._automatic_alpha_tuning:
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result['loss/alpha'] = alpha_loss.item()
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return result
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