import torch import numpy as np from copy import deepcopy from typing import Any, Dict, Tuple, Union, Optional from tianshou.policy import BasePolicy from tianshou.exploration import BaseNoise, GaussianNoise from tianshou.data import Batch, ReplayBuffer class DDPGPolicy(BasePolicy): """Implementation of Deep Deterministic Policy Gradient. arXiv:1509.02971. :param torch.nn.Module actor: the actor network following the rules in :class:`~tianshou.policy.BasePolicy`. (s -> logits) :param torch.optim.Optimizer actor_optim: the optimizer for actor network. :param torch.nn.Module critic: the critic network. (s, a -> Q(s, a)) :param torch.optim.Optimizer critic_optim: the optimizer for critic network. :param action_range: the action range (minimum, maximum). :type action_range: Tuple[float, float] :param float tau: param for soft update of the target network. Default to 0.005. :param float gamma: discount factor, in [0, 1]. Default to 0.99. :param BaseNoise exploration_noise: the exploration noise, add to the action. Default to ``GaussianNoise(sigma=0.1)``. :param bool reward_normalization: normalize the reward to Normal(0, 1), Default to False. :param int estimation_step: the number of steps to look ahead. Default to 1. .. seealso:: Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed explanation. """ def __init__( self, actor: Optional[torch.nn.Module], actor_optim: Optional[torch.optim.Optimizer], critic: Optional[torch.nn.Module], critic_optim: Optional[torch.optim.Optimizer], action_range: Tuple[float, float], tau: float = 0.005, gamma: float = 0.99, exploration_noise: Optional[BaseNoise] = GaussianNoise(sigma=0.1), reward_normalization: bool = False, estimation_step: int = 1, **kwargs: Any, ) -> None: super().__init__(**kwargs) if actor is not None and actor_optim is not None: self.actor: torch.nn.Module = actor self.actor_old = deepcopy(actor) self.actor_old.eval() self.actor_optim: torch.optim.Optimizer = actor_optim if critic is not None and critic_optim is not None: self.critic: torch.nn.Module = critic self.critic_old = deepcopy(critic) self.critic_old.eval() self.critic_optim: torch.optim.Optimizer = critic_optim assert 0.0 <= tau <= 1.0, "tau should be in [0, 1]" self._tau = tau assert 0.0 <= gamma <= 1.0, "gamma should be in [0, 1]" self._gamma = gamma self._noise = exploration_noise self._range = action_range self._action_bias = (action_range[0] + action_range[1]) / 2.0 self._action_scale = (action_range[1] - action_range[0]) / 2.0 # it is only a little difference to use GaussianNoise # self.noise = OUNoise() self._rew_norm = reward_normalization self._n_step = estimation_step def set_exp_noise(self, noise: Optional[BaseNoise]) -> None: """Set the exploration noise.""" self._noise = noise def train(self, mode: bool = True) -> "DDPGPolicy": """Set the module in training mode, except for the target network.""" self.training = mode self.actor.train(mode) self.critic.train(mode) return self def sync_weight(self) -> None: """Soft-update the weight for the target network.""" for o, n in zip(self.actor_old.parameters(), self.actor.parameters()): o.data.copy_(o.data * (1.0 - self._tau) + n.data * self._tau) for o, n in zip(self.critic_old.parameters(), self.critic.parameters()): o.data.copy_(o.data * (1.0 - self._tau) + n.data * self._tau) def _target_q( self, buffer: ReplayBuffer, indice: np.ndarray ) -> torch.Tensor: batch = buffer[indice] # batch.obs_next: s_{t+n} target_q = self.critic_old( batch.obs_next, self(batch, model='actor_old', input='obs_next').act) return target_q def process_fn( self, batch: Batch, buffer: ReplayBuffer, indice: np.ndarray ) -> Batch: batch = self.compute_nstep_return( batch, buffer, indice, self._target_q, self._gamma, self._n_step, self._rew_norm) return batch def forward( self, batch: Batch, state: Optional[Union[dict, Batch, np.ndarray]] = None, model: str = "actor", input: str = "obs", **kwargs: Any, ) -> Batch: """Compute action over the given batch data. :return: A :class:`~tianshou.data.Batch` which has 2 keys: * ``act`` the action. * ``state`` the hidden state. .. seealso:: Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for more detailed explanation. """ model = getattr(self, model) obs = batch[input] actions, h = model(obs, state=state, info=batch.info) actions += self._action_bias actions = actions.clamp(self._range[0], self._range[1]) return Batch(act=actions, state=h) @staticmethod def _mse_optimizer( batch: Batch, critic: torch.nn.Module, optimizer: torch.optim.Optimizer ) -> Tuple[torch.Tensor, torch.Tensor]: """A simple wrapper script for updating critic network.""" weight = getattr(batch, "weight", 1.0) current_q = critic(batch.obs, batch.act).flatten() target_q = batch.returns.flatten() td = current_q - target_q # critic_loss = F.mse_loss(current_q1, target_q) critic_loss = (td.pow(2) * weight).mean() optimizer.zero_grad() critic_loss.backward() optimizer.step() return td, critic_loss def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]: # critic td, critic_loss = self._mse_optimizer( batch, self.critic, self.critic_optim) batch.weight = td # prio-buffer # actor action = self(batch).act actor_loss = -self.critic(batch.obs, action).mean() self.actor_optim.zero_grad() actor_loss.backward() self.actor_optim.step() self.sync_weight() return { "loss/actor": actor_loss.item(), "loss/critic": critic_loss.item(), } def exploration_noise(self, act: np.ndarray, batch: Batch) -> np.ndarray: if self._noise: act = act + self._noise(act.shape) act = act.clip(self._range[0], self._range[1]) return act