# Human-Level Performance in No-Press Diplomacy via Equilibrium Search

arXiv: https://arxiv.org/abs/2010.02923 · HTML: https://mlsanity.com/paper/2010.02923/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 30 of 41)

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- [Modeling Strong and Human-Like Gameplay with KL-Regularized Search](https://mlsanity.com/paper/2112.07544/) (2021)
- [Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning](https://mlsanity.com/paper/2210.05492/) (2022)
- [Evaluating Language Model Agency through Negotiations](https://mlsanity.com/paper/2401.04536/) (2024)
- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](https://mlsanity.com/paper/2107.07630/) (2021)
- [Dynamic Search for Inference-Time Alignment in Diffusion Models](https://mlsanity.com/paper/2503.02039/) (2025)
- [Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy](https://mlsanity.com/paper/2407.06813/) (2024)
- [No-Press Diplomacy from Scratch](https://mlsanity.com/paper/2110.02924/) (2021)
- [ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret](https://mlsanity.com/paper/2206.04122/) (2022)
- [Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination](https://mlsanity.com/paper/2201.12436/) (2022)
- [Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers](https://mlsanity.com/paper/2106.09435/) (2021)
- [Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Information](https://mlsanity.com/paper/2408.02559/) (2024)
- [Solving Common-Payoff Games with Approximate Policy Iteration](https://mlsanity.com/paper/2101.04237/) (2021)
- [Conditional Imitation Learning for Multi-Agent Games](https://mlsanity.com/paper/2201.01448/) (2022)
- [Evaluating Agents using Social Choice Theory](https://mlsanity.com/paper/2312.03121/) (2023)
- [Designing Skill-Compatible AI: Methodologies and Frameworks in Chess](https://mlsanity.com/paper/2405.05066/) (2024)
- [More Victories, Less Cooperation: Assessing Cicero's Diplomacy Play](https://mlsanity.com/paper/2406.04643/) (2024)
- [Normative Disagreement as a Challenge for Cooperative AI](https://mlsanity.com/paper/2111.13872/) (2021)
- [DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy](https://mlsanity.com/paper/2506.09655/) (2025)
- [Personalized Help for Optimizing Low-Skilled Users' Strategy](https://mlsanity.com/paper/2411.09109/) (2024)
- [Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL](https://mlsanity.com/paper/2502.12436/) (2025)
- [Temporal Induced Self-Play for Stochastic Bayesian Games](https://mlsanity.com/paper/2108.09444/) (2021)
- [Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments](https://mlsanity.com/paper/2209.10958/) (2022)
- [AutoReply: Detecting Nonsense in Dialogue Introspectively with Discriminative Replies](https://mlsanity.com/paper/2211.12615/) (2022)
- [NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews](https://mlsanity.com/paper/2411.13779/) (2024)
- [Measuring Fine-Grained Negotiation Tactics of Humans and LLMs in Diplomacy](https://mlsanity.com/paper/2512.18292/) (2025)

## Abstract (arXiv)

Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge. In this paper we describe an agent for the no-press variant of Diplomacy that combines supervised learning on human data with one-step lookahead search via regret minimization. Regret minimization techniques have been behind previous AI successes in adversarial games, most notably poker, but have not previously been shown to be successful in large-scale games involving cooperation. We show that our agent greatly exceeds the performance of past no-press Diplomacy bots, is unexploitable by expert humans, and ranks in the top 2% of human players when playing anonymous games on a popular Diplomacy website.