641a28fbf0daff0ad1ad0f43d2c4b545cb6f9656,examples/reinforcement_learning/tutorial_cartpole_ac.py,Actor,learn,#Actor#Any#Any#Any#,106

Before Change

        //     self.train_op = tf.train.AdamOptimizer(lr).minimize(-self.exp_v)  // minimize(-exp_v) = maximize(exp_v)

    def learn(self, s, a, td):
        _, exp_v = self.sess.run([self.train_op, self.exp_v], {self.s: [s], self.a: [a], self.td_error: td[0]})
        return exp_v

    def choose_action(self, s):
        probs = self.sess.run(self.acts_prob, {self.s: [s]})  // get probabilities of all actions

After Change

    def learn(self, s, a, td):
            // _, exp_v = self.sess.run([self.train_op, self.exp_v], {self.s: [s], self.a: [a], self.td_error: td[0]})
        with tf.GradientTape() as tape:
            _logits = self.model([s]).outputs
            // _probs = tf.nn.softmax(_logits)
            _exp_v = tl.rein.cross_entropy_reward_loss(logits=_logits, actions=[a], rewards=td[0])
        grad = tape.gradient(_exp_v, self.model.weights)
        self.optimizer.apply_gradients(zip(grad, self.model.weights))
        return _exp_v

    def choose_action(self, s):

Instances