149 lines
5.9 KiB
Python
149 lines
5.9 KiB
Python
import torch
|
|
import numpy as np
|
|
from copy import deepcopy
|
|
import torch.nn.functional as F
|
|
|
|
from tianshou.data import Batch
|
|
from tianshou.policy import BasePolicy
|
|
# from tianshou.exploration import OUNoise
|
|
|
|
|
|
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 float tau: param for soft update of the target network, defaults to
|
|
0.005.
|
|
:param float gamma: discount factor, in [0, 1], defaults to 0.99.
|
|
:param float exploration_noise: the noise intensity, add to the action,
|
|
defaults to 0.1.
|
|
:param action_range: the action range (minimum, maximum).
|
|
:type action_range: [float, float]
|
|
:param bool reward_normalization: normalize the reward to Normal(0, 1),
|
|
defaults to ``False``.
|
|
:param bool ignore_done: ignore the done flag while training the policy,
|
|
defaults to ``False``.
|
|
"""
|
|
|
|
def __init__(self, actor, actor_optim, critic, critic_optim,
|
|
tau=0.005, gamma=0.99, exploration_noise=0.1,
|
|
action_range=None, reward_normalization=False,
|
|
ignore_done=False, **kwargs):
|
|
super().__init__(**kwargs)
|
|
if actor is not None:
|
|
self.actor, self.actor_old = actor, deepcopy(actor)
|
|
self.actor_old.eval()
|
|
self.actor_optim = actor_optim
|
|
if critic is not None:
|
|
self.critic, self.critic_old = critic, deepcopy(critic)
|
|
self.critic_old.eval()
|
|
self.critic_optim = critic_optim
|
|
assert 0 <= tau <= 1, 'tau should in [0, 1]'
|
|
self._tau = tau
|
|
assert 0 <= gamma <= 1, 'gamma should in [0, 1]'
|
|
self._gamma = gamma
|
|
assert 0 <= exploration_noise, 'noise should not be negative'
|
|
self._eps = exploration_noise
|
|
assert action_range is not None
|
|
self._range = action_range
|
|
self._action_bias = (action_range[0] + action_range[1]) / 2
|
|
self._action_scale = (action_range[1] - action_range[0]) / 2
|
|
# it is only a little difference to use rand_normal
|
|
# self.noise = OUNoise()
|
|
self._rm_done = ignore_done
|
|
self._rew_norm = reward_normalization
|
|
self.__eps = np.finfo(np.float32).eps.item()
|
|
|
|
def set_eps(self, eps):
|
|
"""Set the eps for exploration."""
|
|
self._eps = eps
|
|
|
|
def train(self):
|
|
"""Set the module in training mode, except for the target network."""
|
|
self.training = True
|
|
self.actor.train()
|
|
self.critic.train()
|
|
|
|
def eval(self):
|
|
"""Set the module in evaluation mode, except for the target network."""
|
|
self.training = False
|
|
self.actor.eval()
|
|
self.critic.eval()
|
|
|
|
def sync_weight(self):
|
|
"""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 - self._tau) + n.data * self._tau)
|
|
for o, n in zip(
|
|
self.critic_old.parameters(), self.critic.parameters()):
|
|
o.data.copy_(o.data * (1 - self._tau) + n.data * self._tau)
|
|
|
|
def process_fn(self, batch, buffer, indice):
|
|
if self._rew_norm:
|
|
bfr = buffer.rew[:min(len(buffer), 1000)] # avoid large buffer
|
|
mean, std = bfr.mean(), bfr.std()
|
|
if std > self.__eps:
|
|
batch.rew = (batch.rew - mean) / std
|
|
if self._rm_done:
|
|
batch.done = batch.done * 0.
|
|
return batch
|
|
|
|
def __call__(self, batch, state=None,
|
|
model='actor', input='obs', eps=None, **kwargs):
|
|
"""Compute action over the given batch data.
|
|
|
|
:param float eps: in [0, 1], for exploration use.
|
|
|
|
:return: A :class:`~tianshou.data.Batch` which has 2 keys:
|
|
|
|
* ``act`` the action.
|
|
* ``state`` the hidden state.
|
|
|
|
More information can be found at
|
|
:meth:`~tianshou.policy.BasePolicy.__call__`.
|
|
"""
|
|
model = getattr(self, model)
|
|
obs = getattr(batch, input)
|
|
logits, h = model(obs, state=state, info=batch.info)
|
|
logits += self._action_bias
|
|
if eps is None:
|
|
eps = self._eps
|
|
if eps > 0:
|
|
# noise = np.random.normal(0, eps, size=logits.shape)
|
|
# logits += torch.tensor(noise, device=logits.device)
|
|
# noise = self.noise(logits.shape, eps)
|
|
logits += torch.randn(
|
|
size=logits.shape, device=logits.device) * eps
|
|
logits = logits.clamp(self._range[0], self._range[1])
|
|
return Batch(act=logits, state=h)
|
|
|
|
def learn(self, batch, **kwargs):
|
|
with torch.no_grad():
|
|
target_q = self.critic_old(batch.obs_next, self(
|
|
batch, model='actor_old', input='obs_next', eps=0).act)
|
|
dev = target_q.device
|
|
rew = torch.tensor(batch.rew,
|
|
dtype=torch.float, device=dev)[:, None]
|
|
done = torch.tensor(batch.done,
|
|
dtype=torch.float, device=dev)[:, None]
|
|
target_q = (rew + (1. - done) * self._gamma * target_q)
|
|
current_q = self.critic(batch.obs, batch.act)
|
|
critic_loss = F.mse_loss(current_q, target_q)
|
|
self.critic_optim.zero_grad()
|
|
critic_loss.backward()
|
|
self.critic_optim.step()
|
|
actor_loss = -self.critic(batch.obs, self(batch, eps=0).act).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(),
|
|
}
|