Question

parl.algorithms.paddle.sac 源代码

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#
# Licensed under the Apache License, Version 2.0 (the "License");
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# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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import parl
import paddle
from paddle.distribution import Normal
import paddle.nn.functional as F
from parl.utils.utils import check_model_method
from copy import deepcopy

__all__ = ['SAC']


[文档]class SAC(parl.Algorithm):
[文档]    def __init__(self,
                 model,
                 gamma=None,
                 tau=None,
                 alpha=None,
                 actor_lr=None,
                 critic_lr=None):
        """ SAC algorithm

        Args:
            model(parl.Model): forward network of actor and critic.
            gamma(float): discounted factor for reward computation
            tau (float): decay coefficient when updating the weights of self.target_model with self.model
            alpha (float): temperature parameter determines the relative importance of the entropy against the reward
            actor_lr (float): learning rate of the actor model
            critic_lr (float): learning rate of the critic model
        """
        # checks
        check_model_method(model, 'value', self.__class__.__name__)
        check_model_method(model, 'policy', self.__class__.__name__)
        check_model_method(model, 'get_actor_params', self.__class__.__name__)
        check_model_method(model, 'get_critic_params', self.__class__.__name__)
        assert isinstance(gamma, float)
        assert isinstance(tau, float)
        assert isinstance(alpha, float)
        assert isinstance(actor_lr, float)
        assert isinstance(critic_lr, float)

        self.gamma = gamma
        self.tau = tau
        self.alpha = alpha
        self.actor_lr = actor_lr
        self.critic_lr = critic_lr

        self.model = model
        self.target_model = deepcopy(self.model)
        self.actor_optimizer = paddle.optimizer.Adam(
            learning_rate=actor_lr, parameters=self.model.get_actor_params())
        self.critic_optimizer = paddle.optimizer.Adam(
            learning_rate=critic_lr, parameters=self.model.get_critic_params())

[文档]    def predict(self, obs):
        act_mean, _ = self.model.policy(obs)
        action = paddle.tanh(act_mean)
        return action

[文档]    def sample(self, obs):
        act_mean, act_log_std = self.model.policy(obs)
        normal = Normal(act_mean, act_log_std.exp())
        # for reparameterization trick  (mean + std*N(0,1))
        x_t = normal.sample([1])
        action = paddle.tanh(x_t)

        log_prob = normal.log_prob(x_t)
        # Enforcing Action Bound
        log_prob -= paddle.log((1 - action.pow(2)) + 1e-6)
        log_prob = paddle.sum(log_prob, axis=-1, keepdim=True)
        return action[0], log_prob[0]

[文档]    def learn(self, obs, action, reward, next_obs, terminal):
        critic_loss = self._critic_learn(obs, action, reward, next_obs,
                                         terminal)
        actor_loss = self._actor_learn(obs)

        self.sync_target()
        return critic_loss, actor_loss

    def _critic_learn(self, obs, action, reward, next_obs, terminal):
        with paddle.no_grad():
            next_action, next_log_pro = self.sample(next_obs)
            q1_next, q2_next = self.target_model.value(next_obs, next_action)
            target_Q = paddle.minimum(q1_next,
                                      q2_next) - self.alpha * next_log_pro
            terminal = paddle.cast(terminal, dtype='float32')
            target_Q = reward + self.gamma * (1. - terminal) * target_Q
        cur_q1, cur_q2 = self.model.value(obs, action)

        critic_loss = F.mse_loss(cur_q1, target_Q) + F.mse_loss(
            cur_q2, target_Q)

        self.critic_optimizer.clear_grad()
        critic_loss.backward()
        self.critic_optimizer.step()
        return critic_loss

    def _actor_learn(self, obs):
        act, log_pi = self.sample(obs)
        q1_pi, q2_pi = self.model.value(obs, act)
        min_q_pi = paddle.minimum(q1_pi, q2_pi)
        actor_loss = ((self.alpha * log_pi) - min_q_pi).mean()

        self.actor_optimizer.clear_grad()
        actor_loss.backward()
        self.actor_optimizer.step()
        return actor_loss

    def sync_target(self, decay=None):
        if decay is None:
            decay = 1.0 - self.tau
        self.model.sync_weights_to(self.target_model, decay=decay)