22a64f59bb70e6d2f75546d726c24a92addabecb,Reinforcement_learning_TUT/5.2_Prioritized_Replay_DQN/RL_brain.py,DoubleDQNPrioritizedReplay,store_transition,#DoubleDQNPrioritizedReplay#Any#Any#Any#Any#,418

Before Change

                                       {self.s: s[np.newaxis, :], self.s_: s_[np.newaxis, :]})
        error = r + np.max(q_next, axis=1) - q_eval[0, a]

        self.memory.store(abs(error[0]), transition)

    def choose_action(self, observation):
        observation = observation[np.newaxis, :]
        if np.random.uniform() < self.epsilon:

After Change

            self.q_next = build_layers(self.s_, c_names, n_l1, w_initializer, b_initializer)

    def store_transition(self, s, a, r, s_):
        if self.prioritized:    // prioritized replay
            transition = np.hstack((s, [a, r], s_))
            q_next, q_eval = self.sess.run([self.q_next, self.q_eval],
                                           {self.s: s[np.newaxis, :], self.s_: s_[np.newaxis, :]})
            error = r + np.max(q_next, axis=1) - q_eval[0, a]
            self.memory.store(abs(error[0]), transition)
        else:       // random replay
            if not hasattr(self, "memory_counter"):
                self.memory_counter = 0
            transition = np.hstack((s, [a, r], s_))
            index = self.memory_counter % self.memory_size
            self.memory.iloc[index, :] = transition
            self.memory_counter += 1

    def choose_action(self, observation):
        observation = observation[np.newaxis, :]
        if np.random.uniform() < self.epsilon:
            actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation})

Instances