Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

  • Yuhang Song University of Oxford
  • Andrzej Wojcicki Lighthouse
  • Thomas Lukasiewicz University of Oxford
  • Jianyi Wang Beihang University
  • Abi Aryan University of California, Los Angeles
  • Zhenghua Xu Hebei University of Technology
  • Mai Xu Beihang University
  • Zihan Ding Imperial College London
  • Lianlong Wu University of Oxford

Abstract

Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide Python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/.

Published
2020-04-03
Section
AAAI Technical Track: Multiagent Systems