# Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games

arXiv: https://arxiv.org/abs/1403.6508 · HTML: https://mlsanity.com/paper/1403.6508/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2014.

## Built on by (most cited first, 17 of 17)

- [Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration](https://mlsanity.com/paper/2108.06038/) (2021)
- [Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning](https://mlsanity.com/paper/2302.10238/) (2023)
- [Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2312.08884/) (2023)
- [Non-Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1911.04220/) (2019)
- [Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Games](https://mlsanity.com/paper/1806.09795/) (2018)
- [Multi-Agent Advisor Q-Learning](https://mlsanity.com/paper/2111.00345/) (2021)
- [Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network](https://mlsanity.com/paper/2102.06854/) (2021)
- [Efficient Inverse Multiagent Learning](https://mlsanity.com/paper/2502.14160/) (2025)
- [Multi-Agent Inverse Reinforcement Learning: Suboptimal Demonstrations and Alternative Solution Concepts](https://mlsanity.com/paper/2109.01178/) (2021)
- [Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations](https://mlsanity.com/paper/2205.02959/) (2022)
- [Inverse Dynamic Games Based on Maximum Entropy Inverse Reinforcement Learning](https://mlsanity.com/paper/1911.07503/) (2019)
- [Soft-Bellman Equilibrium in Affine Markov Games: Forward Solutions and Inverse Learning](https://mlsanity.com/paper/2304.00163/) (2023)
- [EMOTE: An Explainable architecture for Modelling the Other Through Empathy](https://mlsanity.com/paper/2306.00295/) (2023)
- [Inverse Reinforcement Learning for Strategy Identification](https://mlsanity.com/paper/2108.00293/) (2021)
- [Online Parameter Identification of Generalized Non-cooperative Game](https://mlsanity.com/paper/2310.09511/) (2023)
- [Symmetry-Guided Multi-Agent Inverse Reinforcement Learning](https://mlsanity.com/paper/2509.08257/) (2025)
- [Blind Inverse Game Theory: Jointly Decoding Rewards and Rationality in Entropy-Regularized Competitive Games](https://mlsanity.com/paper/2511.05640/) (2025)

## Abstract (arXiv)

The focus of this paper is a Bayesian framework for solving a class of problems termed multi-agent inverse reinforcement learning (MIRL). Compared to the well-known inverse reinforcement learning (IRL) problem, MIRL is formalized in the context of stochastic games, which generalize Markov decision processes to game theoretic scenarios. We establish a theoretical foundation for competitive two-agent zero-sum MIRL problems and propose a Bayesian solution approach in which the generative model is based on an assumption that the two agents follow a minimax bi-policy. Numerical results are presented comparing the Bayesian MIRL method with two existing methods in the context of an abstract soccer game. Investigation centers on relationships between the extent of prior information and the quality of learned rewards. Results suggest that covariance structure is more important than mean value in reward priors.