Is there a multi-agent deep reinforcement learning algorithm which is for environments with only discrete action spaces (Not hybrid)? Is there a multi-agent deep reinforcement learning algorithm which is for environments with only discrete action spaces (Not hybrid) and have centralized training?
I have been looking for algorithms, (A2C, MADDPG etc.) but still havent find any algorithm that provides all of properties i mentioned (Multi agent + discrete action space + deep learning + centralized training).
I am wondering if we use an actor network that gets state as input and concatenated discrete actions of agents as output (For example if agent has 3 actions and we have 4 agents output can be [0,0,1, 0,1,0, 0,0,1, 1,0,0]) is that would be bad idea ?
A natural policy to act in an environment with discrete action space would be a softmax.
This paper describes a method that uses the idea of centralized training, and I believe could be used in your implementation.
With regard to your last question, I don't know if i understood, but if you have a system that must perform 3 actions, you could assign each action to a specific agent (assuming we have three different action spaces). Then you would have a cooperation game with 3 agents, where all of them have a common reward function. In theory, this 3 agents represents an individual agent that interacts with the environment.