Identifying useful appraisal functions in a multi-agent reinforcement learning environment

Abstract

In a multi-agent environment where multiple agents exist, it is often impossible to maximize the rewards of all agents simultaneously due to interference among agents. Therefore, it is difficult to learn cooperative behavior with reinforcement learning, which pursues the maximization of rewards. On the other hand, under the intrinsically motivated reinforcement learning (IMRL) framework, which refers to multiple pieces of information when learning and making decisions, Sequeira et al.\ identified a useful evaluation function for decision making in single-agent environments with genetic programming (GP). In this study, we apply this approach to a multi-agent environment. We test whether GP can identify a useful evaluation function for learning cooperative behavior of multiple independently learning agents to capture some preys in a pursuit problem.