# Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2012.03204 · HTML: https://mlsanity.com/paper/2012.03204/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2020.

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

- [Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration](https://mlsanity.com/paper/2301.03398/) (2023)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games](https://mlsanity.com/paper/2307.01676/) (2023)
- [Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox](https://mlsanity.com/paper/2212.00253/) (2022)
- [Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2412.15517/) (2024)
- [RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL Evaluation and LLM Enhancement](https://mlsanity.com/paper/2412.11417/) (2024)
- [AI in Human-computer Gaming: Techniques, Challenges and Opportunities](https://mlsanity.com/paper/2111.07631/) (2021)
- [Learning cooperative behaviours in adversarial multi-agent systems](https://mlsanity.com/paper/2302.05528/) (2023)
- [OpenGuanDan: A Large-Scale Imperfect Information Game Benchmark](https://mlsanity.com/paper/2602.00676/) (2026)

## Abstract (arXiv)

The development of deep reinforcement learning (DRL) has benefited from the emergency of a variety type of game environments where new challenging problems are proposed and new algorithms can be tested safely and quickly, such as Board games, RTS, FPS, and MOBA games. However, many existing environments lack complexity and flexibility and assume the actions are synchronously executed in multi-agent settings, which become less valuable. We introduce the Fever Basketball game, a novel reinforcement learning environment where agents are trained to play basketball game. It is a complex and challenging environment that supports multiple characters, multiple positions, and both the single-agent and multi-agent player control modes. In addition, to better simulate real-world basketball games, the execution time of actions differs among players, which makes Fever Basketball a novel asynchronized environment. We evaluate commonly used multi-agent algorithms of both independent learners and joint-action learners in three game scenarios with varying difficulties, and heuristically propose two baseline methods to diminish the extra non-stationarity brought by asynchronism in Fever Basketball Benchmarks. Besides, we propose an integrated curricula training (ICT) framework to better handle Fever Basketball problems, which includes several game-rule based cascading curricula learners and a coordination curricula switcher focusing on enhancing coordination within the team. The results show that the game remains challenging and can be used as a benchmark environment for studies like long-time horizon, sparse rewards, credit assignment, and non-stationarity, etc. in multi-agent settings.