Reinforcement learning in multi-agent games: Open AI gym diplomacy environment

Abstract

Reinforcement learning has been successfully applied to adversarial games, exhibiting its potential. However, most real-life scenarios also involve cooperation, in addition to competition. Using reinforcement learning in multi-agent cooperative games is, however, still mostly unexplored. In this paper, a reinforcement learning environment for the Diplomacy board game is presented, using the standard interface adopted by OpenAI Gym environments. Our main purpose is to enable straightforward comparison and reuse of existing reinforcement learning implementations when applied to cooperative games. As a proof-of-concept, we show preliminary results of reinforcement learning agents exploiting this environment.