eval_average_episode_rewards is not defined in shared/mpe_runner.py

[ConstantinosM]

This is a bug that I fixed by taking a look at the other onpolicy code that you have. In shared/mpe_runner.py in def eval() almost at the end of the function:

        eval_episode_rewards = np.array(eval_episode_rewards)
        eval_env_infos = {}
        eval_env_infos['eval_average_episode_rewards'] = np.sum(np.array(eval_episode_rewards), axis=0)
        # print("eval average episode rewards of agent: " + str(eval_average_episode_rewards))

The eval_average_episode_rewards is not defined and code will exit with error. Instead I used:

print("eval average episode rewards of agent: " + str(np.mean(eval_env_infos['eval_average_episode_rewards'])))

This is different than in separated/mpe_runner.py:

        eval_train_infos = []
        for agent_id in range(self.num_agents):
            eval_average_episode_rewards = np.mean(np.sum(eval_episode_rewards[:, :, agent_id], axis=0))
            eval_train_infos.append({'eval_average_episode_rewards': eval_average_episode_rewards})
            print("eval average episode rewards of agent%i: " % agent_id + str(eval_average_episode_rewards))

but i guess the logic is that in the shared case agent1 and agent2 are the same so the average reward between their performance is reasonable.

[zoeyuchao]

Thx~fixed the bug.