Logical Team Q-learning: An approach towards factored policies in cooperative MARL

We address the challenge of learning factored policies in cooperative MARL scenarios. In particular, we consider the situation in which a team of agents collaborates to optimize a common cost. Our goal is to obtain factored policies that determine the individual behavior of each agent so that the resulting joint policy is optimal. In this work we make contributions to both the dynamic programming and reinforcement learning settings. In the dynamic programming case we provide a number of lemmas that prove the existence of such factored policies and we introduce an algorithm (along with proof of convergence) that provably leads to them. Then we introduce tabular and deep versions of Logical Team Q-learning, which is a stochastic version of the algorithm for the RL case. We conclude the paper by providing experiments that illustrate the claims.