M2RL: A Multi-player Multi-agent Reinforcement Learning Framework for Complex Games

Abstract

Distributed deep reinforcement learning (DDRL) has gained increasing attention due to the emerging requirements for addressing complex games like Go and StarCraft. However, how to effectively and stably train bots with asynchronous and heterogeneous agents cooperation and competition for multiple players under multiple machines (with multiple CPUs and GPUs) using DDRL is still an open problem. We propose and open M2RL, a Multi-player and Multi-agent Reinforcement Learning framework, to make training bots for complex games an easy-to-use warehouse. Experiments involving training a two-player multi-agent Wargame AI, and a sixteen-player multi-agent community game Neural MMO AI, demonstrate the effectiveness of the proposed framework by winning a silver award and beating high-level AI bots designed by professional players.