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

arXiv: https://arxiv.org/abs/1905.08085 · HTML: https://mlsanity.com/paper/1905.08085/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 9 of 9)

- [Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot](https://mlsanity.com/paper/2107.06857/) (2021)
- [FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.02081/) (2024)
- [Dealing with Non-Stationarity in MARL via Trust-Region Decomposition](https://mlsanity.com/paper/2102.10616/) (2021)
- [Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games](https://mlsanity.com/paper/2209.10055/) (2022)
- [Offline Multi-agent Reinforcement Learning via Sequential Score Decomposition](https://mlsanity.com/paper/2505.05968/) (2025)
- [Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms](https://mlsanity.com/paper/2109.01768/) (2021)
- [Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning](https://mlsanity.com/paper/2102.04775/) (2021)
- [VECA : A Toolkit for Building Virtual Environments to Train and Test Human-like Agents](https://mlsanity.com/paper/2105.00762/) (2021)
- [Agent-Arena: A General Framework for Evaluating Control Algorithms](https://mlsanity.com/paper/2504.06468/) (2025)

## Abstract (arXiv)

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/.