Thank you @iffiX, again I am gratefull for your feedbacks. Just one last question, how is the state of each agent shared to every critic network of all agents?
What I do not understand is this: If we had decentralized learning, each critic network of every agent i would recieve as input just one vector, the state vector of the agent i. Now that the states are shared, how should I add the state vectors of the other agents into the agent's i critic network?
Should it be a list of tensors, where each element of the list is the state tensor of each agent in the environment?
Finally, I was thinking that the state of agent i must greater affect the policy of the agent i compared to the effect of the state of the agent j or agent z to the policy of agent i. If this is true, the network’s weights will figure this out and adjust the weights in order to make more significant the state vector of agent i?
Thank you in advance!