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请问进行action = model.random_action()的时候，有报错，报错内容如下：File "d:\mutienv\multiagent-particle-envs-master\multiagent\environment.py", line 174, in _set_action
agent.action.u[0] += action[0][1] - action[0][2]
IndexError: index 2 is out of bounds for axis 0 with size 2
action的维度是否不对？进行随机采样时的返回action的是否应该改成5列？
def random_action(self):
return np.random.uniform(low=-1, high=1, size=(self.n_agents, 2))#2
如果改成5列之后，后面还有需要更改的地方吗？

Hi bic4907, i really like your BicNet implementation! My goal is to run your BicNet implementation on an environment where every agent gets -1 reward for each time step it needs to finish the env. But there is a problem with your actor loss implementation, because the loss of the actor is defined as the prediction of the critic, the rewards needs to be zero if the agents performs perfect, isn't it?

Can you explain to me why you implemented it this way? Also is there a possibility that the reward doesn't converges to 0 when the Agents performs good (linke in the environment i mentioned above)?