I am currently exploring multi-agent reinforcement learning. I have multiple agents that communicate with each other and a central service that maintains the environment state.
The central service dispatches some information at regular intervals to all the agents (Lets call this information as energy). The information can be very different for all the agents.
The agents on reception of this information select a particular action. The execution of the action should leave the agent as well as the environment in a positive state. The action requires a limited amount of energy which might change on every timestep. If a agent does not have sufficient energy to it may request for energy from other agents. The other agents may grant or deny this request.
If all the agents are able to successfully perform their actions and leave the environment in a positive state they get a positive reward.
As the environment is stochastic, where a agent's behavior is dependent on another agent can approximate Q Learning be used here?