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
213 lines
8.7 KiB
Python
213 lines
8.7 KiB
Python
from typing import Any, Literal, 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 ObsBatchProtocol, RolloutBatchProtocol
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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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class REDQPolicy(DDPGPolicy):
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"""Implementation of REDQ. arXiv:2101.05982.
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:param actor: The actor network following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> model_output)
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:param actor_optim: The optimizer for actor network.
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:param critic: The critic network. (s, a -> Q(s, a))
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:param critic_optim: The optimizer for critic network.
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:param action_space: Env's action space.
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:param ensemble_size: Number of sub-networks in the critic ensemble.
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:param subset_size: Number of networks in the subset.
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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, then
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alpha is automatically tuned.
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:param exploration_noise: The exploration noise, added to the action. Defaults
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to ``GaussianNoise(sigma=0.1)``.
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:param estimation_step: The number of steps to look ahead.
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:param actor_delay: Number of critic updates before an actor update.
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:param observation_space: Env's observation space.
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:param action_scaling: if True, scale the action from [-1, 1] to the range
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of action_space. Only used if the action_space is continuous.
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:param action_bound_method: method to bound action to range [-1, 1].
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Only used if the action_space is continuous.
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:param lr_scheduler: if not None, will be called in `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.spaces.Box,
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ensemble_size: int = 10,
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subset_size: int = 2,
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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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actor_delay: int = 20,
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exploration_noise: BaseNoise | Literal["default"] | None = None,
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deterministic_eval: bool = True,
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target_mode: Literal["mean", "min"] = "min",
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action_scaling: bool = True,
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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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if target_mode not in ("min", "mean"):
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raise ValueError(f"Unsupported target_mode: {target_mode}")
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if not 0 < subset_size <= ensemble_size:
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raise ValueError(
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f"Invalid choice of ensemble size or subset size. "
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f"Should be 0 < {subset_size=} <= {ensemble_size=}",
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)
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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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self.ensemble_size = ensemble_size
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self.subset_size = subset_size
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self.target_mode = target_mode
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self.critic_gradient_step = 0
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self.actor_delay = actor_delay
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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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# TODO: reduce duplication with SACPolicy
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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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# TODO: make mypy undestand this, or switch to something like pyright...
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alpha = cast(float, alpha)
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self.alpha = alpha
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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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# TODO: why override from the base class?
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def sync_weight(self) -> None:
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for o, n in zip(self.critic_old.parameters(), self.critic.parameters(), strict=True):
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o.data.copy_(o.data * (1.0 - self.tau) + n.data * self.tau)
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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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) -> Batch:
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loc_scale, h = self.actor(batch.obs, state=state, info=batch.info)
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loc, scale = loc_scale
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dist = Independent(Normal(loc, scale), 1)
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act = loc if self.deterministic_eval and not self.training else 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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return Batch(logits=loc_scale, act=squashed_action, state=h, dist=dist, log_prob=log_prob)
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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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a_ = obs_next_result.act
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sample_ensemble_idx = np.random.choice(self.ensemble_size, self.subset_size, replace=False)
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qs = self.critic_old(obs_next_batch.obs, a_)[sample_ensemble_idx, ...]
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if self.target_mode == "min":
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target_q, _ = torch.min(qs, dim=0)
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elif self.target_mode == "mean":
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target_q = torch.mean(qs, dim=0)
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target_q -= self.alpha * obs_next_result.log_prob
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return target_q
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def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> dict[str, float]:
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# critic ensemble
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weight = getattr(batch, "weight", 1.0)
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current_qs = self.critic(batch.obs, batch.act).flatten(1)
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target_q = batch.returns.flatten()
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td = current_qs - target_q
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critic_loss = (td.pow(2) * weight).mean()
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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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batch.weight = torch.mean(td, dim=0) # prio-buffer
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self.critic_gradient_step += 1
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# actor
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if self.critic_gradient_step % self.actor_delay == 0:
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obs_result = self(batch)
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a = obs_result.act
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current_qa = self.critic(batch.obs, a).mean(dim=0).flatten()
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actor_loss = (self.alpha * obs_result.log_prob.flatten() - current_qa).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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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 = {"loss/critics": critic_loss.item()}
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if self.critic_gradient_step % self.actor_delay == 0:
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result["loss/actor"] = (actor_loss.item(),)
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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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