Enhancing Deep Reinforcement Learning Approaches for Multi-Robot Navigation via Single-Robot Evolutionary Policy Search

Recent Multi-Agent Deep Reinforcement Learning approaches factorize a global action-value to address non-stationarity and favor cooperation. These methods, however, hinder exploration by introducing constraints (e.g., additive value-decomposition) to guarantee the factorization. Our goal is to enhance exploration and improve sample efficiency of multi-robot mapless navigation by incorporating a periodical Evolutionary Policy Search (EPS). In detail, the multi-agent training “specializes” the robots' policies to learn the collision avoidance skills that are mandatory for the task. Concurrently, in this work we propose the use of Evolutionary Algorithms to explore different regions of the policy space in an environment with only a single robot. The idea is that core navigation skills, originated by the multi-robot policies using mutation operators, improve faster in the single-robot EPS. Hence, policy parameters can be injected into the multi-robot setting using crossovers, leading to improved performance and sample efficiency. Experiments in tasks with up to 12 robots confirm the beneficial transfer of navigation skills from the EPS to the multi-robot setting, improving the performance of prior methods.