# An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective

**Authors:** Yaodong Yang, Jun Wang  
**arXiv:** [2011.00583](https://arxiv.org/abs/2011.00583)  
**Date:** 2020-11-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2011.00583v3/

## Key points

- Significant advancements in multi-agent reinforcement learning (MARL) techniques in 2019
- MARL involves multiple agents learning simultaneously in a multi-agent system
- Interdisciplinary field drawing from game theory, machine learning, stochastic control, psychology, and optimization
- Lack of comprehensive literature exploring game theoretical foundations and recent developments in MARL methods
- Existing surveys on MARL are outdated and do not cover progress made since 2010
- Goal is to offer a self-contained assessment of state-of-the-art MARL techniques from a game theoretical standpoint
- Serves as a valuable resource for new researchers and existing experts in the field
- Aims to be a stepping stone towards further advancements in MARL by covering both fundamentals and latest developments

## Summary

In 2019, there were significant advancements in multi-agent reinforcement learning (MARL) techniques following the success of the AlphaGO series. MARL involves multiple agents learning simultaneously in a multi-agent system. This interdisciplinary field draws from game theory, machine learning, stochastic control, psychology and optimization. While MARL has shown empirical success in solving real-world games, there is a lack of comprehensive literature that explores the game theoretical foundations and recent developments in MARL methods. Existing surveys on MARL are outdated and do not fully cover the progress made since 2010. To address this gap, their goal is to offer a self-contained assessment of state-of-the-art MARL techniques from a game theoretical standpoint. This monograph serves as a valuable resource for both new researchers entering this rapidly growing domain and existing experts seeking a panoramic view and identification of new directions based on recent advances. By covering both the fundamentals and latest developments in the research frontier, this work aims to be a stepping stone towards further advancements in MARL.