# Finding Friend and Foe in Multi-Agent Games

arXiv: https://arxiv.org/abs/1906.02330 · HTML: https://mlsanity.com/paper/1906.02330/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 30 of 42)

- [Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game](https://mlsanity.com/paper/2310.18940/) (2023)
- [Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents](https://mlsanity.com/paper/2404.16698/) (2024)
- [Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation](https://mlsanity.com/paper/2310.01320/) (2023)
- [Enhance Reasoning for Large Language Models in the Game Werewolf](https://mlsanity.com/paper/2402.02330/) (2024)
- [Deep Interactive Bayesian Reinforcement Learning via Meta-Learning](https://mlsanity.com/paper/2101.03864/) (2021)
- [ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret](https://mlsanity.com/paper/2206.04122/) (2022)
- [Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf](https://mlsanity.com/paper/2405.19946/) (2024)
- [Modeling Multimodal Social Interactions: New Challenges and Baselines with Densely Aligned Representations](https://mlsanity.com/paper/2403.02090/) (2024)
- [Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization](https://mlsanity.com/paper/2502.04686/) (2025)
- [Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination](https://mlsanity.com/paper/2504.12714/) (2025)
- [Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models](https://mlsanity.com/paper/2311.05720/) (2023)
- [Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games](https://mlsanity.com/paper/2212.08279/) (2022)
- [Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game](https://mlsanity.com/paper/2501.14225/) (2025)
- [Bayesian Social Deduction with Graph-Informed Language Models](https://mlsanity.com/paper/2506.17788/) (2025)
- [Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria](https://mlsanity.com/paper/2201.01816/) (2022)
- [DVM: Towards Controllable LLM Agents in Social Deduction Games](https://mlsanity.com/paper/2501.06695/) (2025)
- [Modeling Others' Minds as Code](https://mlsanity.com/paper/2510.01272/) (2025)
- [Evaluating LLMs in Open-Source Games](https://mlsanity.com/paper/2512.00371/) (2025)
- [A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games](https://mlsanity.com/paper/2207.08894/) (2022)
- [Training an Assassin AI for The Resistance: Avalon](https://mlsanity.com/paper/2209.09331/) (2022)
- [Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL](https://mlsanity.com/paper/2502.12436/) (2025)
- [The Stackelberg Speaker: Optimizing Persuasive Communication in Social Deduction Games](https://mlsanity.com/paper/2510.09087/) (2025)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Temporal Induced Self-Play for Stochastic Bayesian Games](https://mlsanity.com/paper/2108.09444/) (2021)
- [Learning not to Regret](https://mlsanity.com/paper/2303.01074/) (2023)
- [Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning](https://mlsanity.com/paper/2310.04796/) (2023)
- [MafiaScope: Non-Invasive, Time-Resolved Belief Probing for LLM Agents in Social Deduction Games](https://mlsanity.com/paper/2607.10645/) (2026)
- [Polymatrix Competitive Gradient Descent](https://mlsanity.com/paper/2111.08565/) (2021)
- [Configurable Mirror Descent: Towards a Unification of Decision Making](https://mlsanity.com/paper/2405.11746/) (2024)
- [CSP4SDG: Constraint and Information-Theory Based Role Identification in Social Deduction Games with LLM-Enhanced Inference](https://mlsanity.com/paper/2511.06175/) (2025)

## Abstract (arXiv)

Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play. Our algorithm integrates deductive reasoning into vector-form CFR to reason about joint beliefs and deduce partially observable actions. We augment deep value networks with constraints that yield interpretable representations of win probabilities. These innovations enable DeepRole to scale to the full Avalon game. Empirical game-theoretic methods show that DeepRole outperforms other hand-crafted and learned agents in five-player Avalon. DeepRole played with and against human players on the web in hybrid human-agent teams. We find that DeepRole outperforms human players as both a cooperator and a competitor.