# On Multi-Agent Learning in Team Sports Games

arXiv: https://arxiv.org/abs/1906.10124 · HTML: https://mlsanity.com/paper/1906.10124/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation](https://mlsanity.com/paper/2103.04972/) (2021)
- [Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games](https://mlsanity.com/paper/2303.02160/) (2023)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL](https://mlsanity.com/paper/2102.11893/) (2021)
- [Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi](https://mlsanity.com/paper/2412.06333/) (2024)
- [Credit-cognisant reinforcement learning for multi-agent cooperation](https://mlsanity.com/paper/2211.10100/) (2022)

## Abstract (arXiv)

In recent years, reinforcement learning has been successful in solving video games from Atari to Star Craft II. However, the end-to-end model-free reinforcement learning (RL) is not sample efficient and requires a significant amount of computational resources to achieve superhuman level performance. Model-free RL is also unlikely to produce human-like agents for playtesting and gameplaying AI in the development cycle of complex video games. In this paper, we present a hierarchical approach to training agents with the goal of achieving human-like style and high skill level in team sports games. While this is still work in progress, our preliminary results show that the presented approach holds promise for solving the posed multi-agent learning problem.