TEAM POLICY LEARNING FOR MULTI-AGENT REINFORCEMENT LEARNING

This work presents a fully distributed algorithm for learning the optimal policy in a multi-agent cooperative reinforcement learning scenario. We focus on games that can only be solved through coordinated team work. We consider situations in which K players interact simultaneously with an environment and with each other to attain a common goal. In the algorithm, agents only communicate with other agents in their immediate neighborhood and choose their actions independently of one another based only on local information. Learning is done off-policy, which results in high data efficiency. The proposed algorithm is of the stochastic primal-dual kind and can be shown to converge even when used in conjunction with a wide class of function approximators.