# Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games

arXiv: https://arxiv.org/abs/2306.05216 · HTML: https://mlsanity.com/paper/2306.05216/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2023.

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

- [Policy Space Response Oracles: A Survey](https://mlsanity.com/paper/2403.02227/) (2024)
- [Maximizing utility in multi-agent environments by anticipating the behavior of other learners](https://mlsanity.com/paper/2407.04889/) (2024)
- [Computational Intractability of Strategizing against Online Learners](https://mlsanity.com/paper/2503.04202/) (2025)
- [Deep Reinforcement Learning for Sequential Combinatorial Auctions](https://mlsanity.com/paper/2407.08022/) (2024)
- [Learning Recommender Mechanisms for Bayesian Stochastic Games](https://mlsanity.com/paper/2505.22979/) (2025)
- [PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense](https://mlsanity.com/paper/2508.19488/) (2025)

## Abstract (arXiv)

We introduce a new approach for computing optimal equilibria via learning in games. It applies to extensive-form settings with any number of players, including mechanism design, information design, and solution concepts such as correlated, communication, and certification equilibria. We observe that optimal equilibria are minimax equilibrium strategies of a player in an extensive-form zero-sum game. This reformulation allows to apply techniques for learning in zero-sum games, yielding the first learning dynamics that converge to optimal equilibria, not only in empirical averages, but also in iterates. We demonstrate the practical scalability and flexibility of our approach by attaining state-of-the-art performance in benchmark tabular games, and by computing an optimal mechanism for a sequential auction design problem using deep reinforcement learning.