bf39b9ef7d
Add an indicator(i.e. `self.learning`) of learning will be convenient for distinguishing state of policy. Meanwhile, the state of `self.training` will be undisputed in the training stage. Related issue: #211 Others: - fix a bug in DDQN: target_q could not be sampled from np.random.rand - fix a bug in DQN atari net: it should add a ReLU before the last layer - fix a bug in collector timing Co-authored-by: n+e <463003665@qq.com>
170 lines
6.5 KiB
Python
170 lines
6.5 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 Any, Dict, Tuple, Union, Optional
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from tianshou.policy import BasePolicy
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from tianshou.exploration import BaseNoise, GaussianNoise
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from tianshou.data import Batch, ReplayBuffer, to_torch_as
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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 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 critic: the critic network. (s, a -> Q(s, a))
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:param torch.optim.Optimizer critic_optim: the optimizer for critic
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network.
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:param action_range: the action range (minimum, maximum).
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:type action_range: Tuple[float, float]
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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 BaseNoise exploration_noise: the exploration noise,
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add to the action, defaults to ``GaussianNoise(sigma=0.1)``.
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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 int estimation_step: greater than 1, the number of steps to look
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ahead.
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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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actor: Optional[torch.nn.Module],
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actor_optim: Optional[torch.optim.Optimizer],
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critic: Optional[torch.nn.Module],
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critic_optim: Optional[torch.optim.Optimizer],
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action_range: Tuple[float, float],
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tau: float = 0.005,
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gamma: float = 0.99,
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exploration_noise: Optional[BaseNoise] = GaussianNoise(sigma=0.1),
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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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**kwargs: Any,
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) -> None:
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super().__init__(**kwargs)
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if actor is not None and actor_optim is not None:
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self.actor: torch.nn.Module = 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: torch.optim.Optimizer = actor_optim
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if critic is not None and critic_optim is not None:
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self.critic: torch.nn.Module = 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: torch.optim.Optimizer = critic_optim
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assert 0.0 <= tau <= 1.0, "tau should be in [0, 1]"
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self._tau = tau
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assert 0.0 <= gamma <= 1.0, "gamma should be in [0, 1]"
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self._gamma = gamma
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self._noise = exploration_noise
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self._range = action_range
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self._action_bias = (action_range[0] + action_range[1]) / 2.0
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self._action_scale = (action_range[1] - action_range[0]) / 2.0
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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._rm_done = ignore_done
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self._rew_norm = reward_normalization
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assert estimation_step > 0, "estimation_step should be greater than 0"
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self._n_step = estimation_step
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def set_exp_noise(self, noise: Optional[BaseNoise]) -> None:
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"""Set the exploration noise."""
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self._noise = noise
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def train(self, mode: bool = True) -> "DDPGPolicy":
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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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for o, n in zip(self.actor_old.parameters(), self.actor.parameters()):
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o.data.copy_(o.data * (1.0 - self._tau) + n.data * self._tau)
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for o, n in zip(
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self.critic_old.parameters(), self.critic.parameters()
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):
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o.data.copy_(o.data * (1.0 - self._tau) + n.data * self._tau)
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def _target_q(
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self, buffer: ReplayBuffer, indice: np.ndarray
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) -> torch.Tensor:
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batch = buffer[indice] # batch.obs_next: s_{t+n}
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with torch.no_grad():
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target_q = self.critic_old(
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batch.obs_next,
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self(batch, model='actor_old', input='obs_next').act)
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return target_q
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def process_fn(
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self, batch: Batch, buffer: ReplayBuffer, indice: np.ndarray
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) -> Batch:
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if self._rm_done:
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batch.done = batch.done * 0.0
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batch = self.compute_nstep_return(
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batch, buffer, indice, self._target_q,
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self._gamma, self._n_step, self._rew_norm)
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return batch
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def forward(
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self,
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batch: Batch,
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state: Optional[Union[dict, Batch, np.ndarray]] = None,
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model: str = "actor",
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input: str = "obs",
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**kwargs: Any,
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) -> Batch:
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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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obs = batch[input]
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actions, h = model(obs, state=state, info=batch.info)
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actions += self._action_bias
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if self._noise and not self.updating:
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actions += to_torch_as(self._noise(actions.shape), actions)
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actions = actions.clamp(self._range[0], self._range[1])
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return Batch(act=actions, state=h)
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def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
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weight = batch.pop("weight", 1.0)
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current_q = self.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 = (td.pow(2) * weight).mean()
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batch.weight = td # prio-buffer
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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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action = self(batch).act
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actor_loss = -self.critic(batch.obs, action).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 {
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"loss/actor": actor_loss.item(),
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"loss/critic": critic_loss.item(),
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}
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