a95eb8bd878b43346c4bb2e8e29911dc1ab90638,A2C.py,A2C,learn,#A2C#,49

Before Change

            returns = np.concatenate([discount_rewards(trajectory["reward"], config["gamma"]) for trajectory in trajectories])
            qw_new = self.get_critic_value(all_state)

            self.sess.run([self.critic_train], feed_dict={self.critic_state_in: all_state, self.critic_target: returns})  // Reshape or not?
            self.sess.run([self.actor_train], feed_dict={self.actor_input: all_state, self.actions_taken: all_action, self.critic_feedback: qw_new, self.critic_rewards: returns})

            episode_rewards = np.array([trajectory["reward"].sum() for trajectory in trajectories])  // episode total rewards

After Change

                                    self.episode_lengths: np.mean(episode_lengths)
                                    })
            self.writer.add_summary(results[0], iteration)
            self.writer.flush()

            reporter.print_iteration_stats(iteration, episode_rewards, episode_lengths, total_n_trajectories)

class A2CDiscrete(A2C):

Instances