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
211 lines
8.2 KiB
Python
211 lines
8.2 KiB
Python
import warnings
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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 tianshou.data import Batch, ReplayBuffer
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from tianshou.data.batch import BatchProtocol
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from tianshou.data.types import (
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ActStateBatchProtocol,
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BatchWithReturnsProtocol,
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ObsBatchProtocol,
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RolloutBatchProtocol,
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)
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from tianshou.exploration import BaseNoise, GaussianNoise
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from tianshou.policy import BasePolicy
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from tianshou.policy.base import TLearningRateScheduler
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class DDPGPolicy(BasePolicy):
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"""Implementation of Deep Deterministic Policy Gradient. arXiv:1509.02971.
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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 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 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 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.Space,
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tau: float = 0.005,
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gamma: float = 0.99,
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exploration_noise: BaseNoise | Literal["default"] | None = "default",
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estimation_step: int = 1,
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observation_space: gym.Space | None = None,
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action_scaling: bool = True,
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# tanh not supported, see assert below
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action_bound_method: Literal["clip"] | None = "clip",
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lr_scheduler: TLearningRateScheduler | None = None,
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) -> None:
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assert 0.0 <= tau <= 1.0, f"tau should be in [0, 1] but got: {tau}"
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assert 0.0 <= gamma <= 1.0, f"gamma should be in [0, 1] but got: {gamma}"
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assert action_bound_method != "tanh", ( # type: ignore[comparison-overlap]
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"tanh mapping is not supported"
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"in policies where action is used as input of critic , because"
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"raw action in range (-inf, inf) will cause instability in training"
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)
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super().__init__(
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action_space=action_space,
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observation_space=observation_space,
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action_scaling=action_scaling,
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action_bound_method=action_bound_method,
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lr_scheduler=lr_scheduler,
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)
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if action_scaling and not np.isclose(actor.max_action, 1.0): # type: ignore
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warnings.warn(
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"action_scaling and action_bound_method are only intended to deal"
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"with unbounded model action space, but find actor model bound"
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f"action space with max_action={actor.max_action}."
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"Consider using unbounded=True option of the actor model,"
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"or set action_scaling to False and action_bound_method to None.",
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)
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self.actor = actor
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self.actor_old = deepcopy(actor)
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self.actor_old.eval()
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self.actor_optim = actor_optim
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self.critic = critic
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self.critic_old = deepcopy(critic)
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self.critic_old.eval()
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self.critic_optim = critic_optim
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self.tau = tau
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self.gamma = gamma
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if exploration_noise == "default":
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exploration_noise = GaussianNoise(sigma=0.1)
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# TODO: IMPORTANT - can't call this "exploration_noise" because confusingly,
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# there is already a method called exploration_noise() in the base class
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# Now this method doesn't apply any noise and is also not overridden. See TODO there
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self._exploration_noise = exploration_noise
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# it is only a little difference to use GaussianNoise
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# self.noise = OUNoise()
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self.estimation_step = estimation_step
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def set_exp_noise(self, noise: BaseNoise | None) -> None:
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"""Set the exploration noise."""
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self._exploration_noise = noise
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def train(self, mode: bool = True) -> Self:
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"""Set the module in training mode, except for the target network."""
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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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return self
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def sync_weight(self) -> None:
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"""Soft-update the weight for the target network."""
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self.soft_update(self.actor_old, self.actor, self.tau)
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self.soft_update(self.critic_old, self.critic, self.tau)
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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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return self.critic_old(obs_next_batch.obs, self(obs_next_batch, model="actor_old").act)
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def process_fn(
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self,
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batch: RolloutBatchProtocol,
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buffer: ReplayBuffer,
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indices: np.ndarray,
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) -> RolloutBatchProtocol | BatchWithReturnsProtocol:
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return self.compute_nstep_return(
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batch=batch,
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buffer=buffer,
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indices=indices,
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target_q_fn=self._target_q,
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gamma=self.gamma,
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n_step=self.estimation_step,
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)
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def forward(
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self,
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batch: ObsBatchProtocol,
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state: dict | BatchProtocol | np.ndarray | None = None,
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model: Literal["actor", "actor_old"] = "actor",
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**kwargs: Any,
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) -> ActStateBatchProtocol:
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"""Compute action over the given batch data.
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:return: A :class:`~tianshou.data.Batch` which has 2 keys:
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* ``act`` the action.
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* ``state`` the hidden state.
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.. seealso::
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Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
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more detailed explanation.
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"""
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model = getattr(self, model)
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actions, hidden = model(batch.obs, state=state, info=batch.info)
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return cast(ActStateBatchProtocol, Batch(act=actions, state=hidden))
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@staticmethod
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def _mse_optimizer(
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batch: RolloutBatchProtocol,
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critic: torch.nn.Module,
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optimizer: torch.optim.Optimizer,
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""A simple wrapper script for updating critic network."""
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weight = getattr(batch, "weight", 1.0)
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current_q = critic(batch.obs, batch.act).flatten()
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target_q = batch.returns.flatten()
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td = current_q - target_q
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# critic_loss = F.mse_loss(current_q1, target_q)
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critic_loss = (td.pow(2) * weight).mean()
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optimizer.zero_grad()
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critic_loss.backward()
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optimizer.step()
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return td, critic_loss
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def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> dict[str, float]:
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# critic
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td, critic_loss = self._mse_optimizer(batch, self.critic, self.critic_optim)
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batch.weight = td # prio-buffer
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# actor
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actor_loss = -self.critic(batch.obs, self(batch).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 {"loss/actor": actor_loss.item(), "loss/critic": critic_loss.item()}
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def exploration_noise(
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self,
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act: np.ndarray | BatchProtocol,
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batch: RolloutBatchProtocol,
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) -> np.ndarray | BatchProtocol:
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if self._exploration_noise is None:
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return act
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if isinstance(act, np.ndarray):
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return act + self._exploration_noise(act.shape)
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warnings.warn("Cannot add exploration noise to non-numpy_array action.")
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return act
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