3a1bc18add
This PR adds a new method for getting actions from an env's observation and info. This is useful for standard inference and stands in contrast to batch-based methods that are currently used in training and evaluation. Without this, users have to do some kind of gymnastics to actually perform inference with a trained policy. I have also added a test for the new method. In future PRs, this method should be included in the examples (in the the "watch" section). To add this required improving multiple typing things and, importantly, _simplifying the signature of `forward` in many policies!_ This is a **breaking change**, but it will likely affect no users. The `input` parameter of forward was a rather hacky mechanism, I believe it is good that it's gone now. It will also help with #948 . The main functional change is the addition of `compute_action` to `BasePolicy`. Other minor changes: - improvements in typing - updated PR and Issue templates - Improved handling of `max_action_num` Closes #981
238 lines
9.5 KiB
Python
238 lines
9.5 KiB
Python
from copy import deepcopy
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from typing import Any, Literal, Self, cast
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import gymnasium as gym
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import numpy as np
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import torch
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from torch.distributions import Independent, Normal
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from tianshou.data import Batch, ReplayBuffer
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from tianshou.data.types import (
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DistLogProbBatchProtocol,
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ObsBatchProtocol,
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RolloutBatchProtocol,
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)
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from tianshou.exploration import BaseNoise
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from tianshou.policy import DDPGPolicy
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from tianshou.policy.base import TLearningRateScheduler
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from tianshou.utils.optim import clone_optimizer
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class SACPolicy(DDPGPolicy):
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"""Implementation of Soft Actor-Critic. arXiv:1812.05905.
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:param actor: the actor network following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> logits)
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:param actor_optim: the optimizer for actor network.
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:param critic: the first critic network. (s, a -> Q(s, a))
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:param critic_optim: the optimizer for the first critic network.
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:param action_space: Env's action space. Should be gym.spaces.Box.
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:param critic2: the second critic network. (s, a -> Q(s, a)).
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If None, use the same network as critic (via deepcopy).
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:param critic2_optim: the optimizer for the second critic network.
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If None, clone critic_optim to use for critic2.parameters().
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:param tau: param for soft update of the target network.
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:param gamma: discount factor, in [0, 1].
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:param alpha: entropy regularization coefficient.
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If a tuple (target_entropy, log_alpha, alpha_optim) is provided,
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then alpha is automatically tuned.
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:param estimation_step: The number of steps to look ahead.
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:param exploration_noise: add noise to action for exploration.
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This is useful when solving "hard exploration" problems.
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"default" is equivalent to GaussianNoise(sigma=0.1).
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:param deterministic_eval: whether to use deterministic action
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(mean of Gaussian policy) in evaluation mode instead of stochastic
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action sampled by the policy. Does not affect training.
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:param action_scaling: whether to map actions from range [-1, 1]
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to range[action_spaces.low, action_spaces.high].
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:param action_bound_method: method to bound action to range [-1, 1],
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can be either "clip" (for simply clipping the action)
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or empty string for no bounding. Only used if the action_space is continuous.
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:param observation_space: Env's observation space.
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:param lr_scheduler: a learning rate scheduler that adjusts the learning rate
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in optimizer in each policy.update()
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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__(
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self,
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*,
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actor: torch.nn.Module,
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actor_optim: torch.optim.Optimizer,
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critic: torch.nn.Module,
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critic_optim: torch.optim.Optimizer,
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action_space: gym.Space,
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critic2: torch.nn.Module | None = None,
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critic2_optim: torch.optim.Optimizer | None = None,
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tau: float = 0.005,
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gamma: float = 0.99,
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alpha: float | tuple[float, torch.Tensor, torch.optim.Optimizer] = 0.2,
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estimation_step: int = 1,
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exploration_noise: BaseNoise | Literal["default"] | None = None,
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deterministic_eval: bool = True,
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action_scaling: bool = True,
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# TODO: some papers claim that tanh is crucial for SAC, yet DDPG will raise an
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# error if tanh is used. Should be investigated.
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action_bound_method: Literal["clip"] | None = "clip",
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observation_space: gym.Space | None = None,
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lr_scheduler: TLearningRateScheduler | None = None,
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) -> None:
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super().__init__(
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actor=actor,
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actor_optim=actor_optim,
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critic=critic,
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critic_optim=critic_optim,
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action_space=action_space,
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tau=tau,
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gamma=gamma,
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exploration_noise=exploration_noise,
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estimation_step=estimation_step,
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action_scaling=action_scaling,
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action_bound_method=action_bound_method,
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observation_space=observation_space,
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lr_scheduler=lr_scheduler,
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)
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critic2 = critic2 or deepcopy(critic)
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critic2_optim = critic2_optim or clone_optimizer(critic_optim, critic2.parameters())
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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.deterministic_eval = deterministic_eval
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self.__eps = np.finfo(np.float32).eps.item()
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self.alpha: float | torch.Tensor
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self._is_auto_alpha = not isinstance(alpha, float)
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if self._is_auto_alpha:
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# TODO: why doesn't mypy understand that this must be a tuple?
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alpha = cast(tuple[float, torch.Tensor, torch.optim.Optimizer], alpha)
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if alpha[1].shape != torch.Size([1]):
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raise ValueError(
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f"Expected log_alpha to have shape torch.Size([1]), "
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f"but got {alpha[1].shape} instead.",
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)
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if not alpha[1].requires_grad:
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raise ValueError("Expected log_alpha to require gradient, but it doesn't.")
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self.target_entropy, self.log_alpha, self.alpha_optim = alpha
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self.alpha = self.log_alpha.detach().exp()
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else:
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alpha = cast(float, alpha)
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self.alpha = alpha
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# TODO or not TODO: add to BasePolicy?
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self._check_field_validity()
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def _check_field_validity(self) -> None:
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if not isinstance(self.action_space, gym.spaces.Box):
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raise ValueError(
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f"SACPolicy only supports gym.spaces.Box, but got {self.action_space=}."
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f"Please use DiscreteSACPolicy for discrete action spaces.",
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)
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@property
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def is_auto_alpha(self) -> bool:
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return self._is_auto_alpha
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def train(self, mode: bool = True) -> Self:
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self.training = mode
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self.actor.train(mode)
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self.critic.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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self.soft_update(self.critic_old, self.critic, self.tau)
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self.soft_update(self.critic2_old, self.critic2, self.tau)
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# TODO: violates Liskov substitution principle
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def forward( # type: ignore
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self,
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batch: ObsBatchProtocol,
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state: dict | Batch | np.ndarray | None = None,
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**kwargs: Any,
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) -> DistLogProbBatchProtocol:
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logits, hidden = self.actor(batch.obs, state=state, info=batch.info)
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assert isinstance(logits, tuple)
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dist = Independent(Normal(*logits), 1)
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if self.deterministic_eval and not self.training:
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act = logits[0]
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else:
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act = dist.rsample()
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log_prob = dist.log_prob(act).unsqueeze(-1)
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# apply correction for Tanh squashing when computing logprob from Gaussian
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# You can check out the original SAC paper (arXiv 1801.01290): Eq 21.
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# in appendix C to get some understanding of this equation.
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squashed_action = torch.tanh(act)
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log_prob = log_prob - torch.log((1 - squashed_action.pow(2)) + self.__eps).sum(
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-1,
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keepdim=True,
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)
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result = Batch(
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logits=logits,
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act=squashed_action,
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state=hidden,
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dist=dist,
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log_prob=log_prob,
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)
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return cast(DistLogProbBatchProtocol, result)
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def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor:
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obs_next_batch = Batch(
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obs=buffer[indices].obs_next,
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info=[None] * len(indices),
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) # obs_next: s_{t+n}
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obs_next_result = self(obs_next_batch)
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act_ = obs_next_result.act
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return (
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torch.min(
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self.critic_old(obs_next_batch.obs, act_),
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self.critic2_old(obs_next_batch.obs, act_),
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)
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- self.alpha * obs_next_result.log_prob
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)
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def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> dict[str, float]:
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# critic 1&2
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td1, critic1_loss = self._mse_optimizer(batch, self.critic, self.critic_optim)
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td2, critic2_loss = self._mse_optimizer(batch, self.critic2, self.critic2_optim)
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batch.weight = (td1 + td2) / 2.0 # prio-buffer
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# actor
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obs_result = self(batch)
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act = obs_result.act
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current_q1a = self.critic(batch.obs, act).flatten()
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current_q2a = self.critic2(batch.obs, act).flatten()
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actor_loss = (
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self.alpha * obs_result.log_prob.flatten() - torch.min(current_q1a, current_q2a)
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).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.is_auto_alpha:
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log_prob = obs_result.log_prob.detach() + self.target_entropy
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# please take a look at issue #258 if you'd like to change this line
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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.detach().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.is_auto_alpha:
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self.alpha = cast(torch.Tensor, self.alpha)
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result["loss/alpha"] = alpha_loss.item()
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result["alpha"] = self.alpha.item()
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return result
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