Arena：一个通用的多Agent智能评估平台和构建工具
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
摘要：
学习代理不仅能够参加测试，而且还能够创新，这正在成为人工智能的热门话题。实现这一愿景的最有前途的途径之一是多智能体学习，其中智能体充当彼此的环境，改进每个智能体意味着为其他智能体提出新的问题。然而，现有的评估平台要么与多智能体设置不兼容，要么仅限于特定的游戏。也就是说，目前还没有一个通用的多智能体智能研究的评价平台。为此，我们介绍了竞技场，一个通用的多智能体智能评估平台，35个游戏的不同逻辑和表示。此外，多智能体智能仍处于许多问题尚未探索的阶段。因此，我们提供了一个建设工具包，研究人员可以很容易地发明和建立新的多智能体问题，从提供的游戏集的基础上GUI可配置的社会树和五个基本的多智能体奖励计划。最后，我们提供了五个最先进的深度多智能体强化学习基线的Python实现。随着基线实现，我们发布了一组100个最好的代理/团队，我们可以训练不同的训练方案，为每个游戏，作为评估代理与人口的表现的基础。因此，研究界可以在一个稳定和统一的标准下进行比较。所有的实现和附带的教程都已经在https://sites.google.com/view/arena-unity/上为社区开源。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
