  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. The goal is to obtain factored policies
that determine the individual behavior of each agent so that the resulting
joint policy is optimal. The main contribution of this work is the introduction
of Logical Team Q-learning (LTQL). LTQL does not rely on assumptions about the
environment and hence is generally applicable to any collaborative MARL
scenario. We derive LTQL as a stochastic approximation to a dynamic programming
method we introduce in this work. We conclude the paper by providing
experiments (both in the tabular and deep settings) that illustrate the claims.
