# Approximating Nash Equilibria in General-Sum Games via Meta-Learning

arXiv: https://arxiv.org/abs/2504.18868 · HTML: https://mlsanity.com/paper/2504.18868/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 26 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker](https://mlsanity.com/paper/1701.01724/) (2017)
- [Combining Deep Reinforcement Learning and Search for Imperfect-Information Games](https://mlsanity.com/paper/2007.13544/) (2020)
- [Deep Counterfactual Regret Minimization](https://mlsanity.com/paper/1811.00164/) (2018)
- [Solving Large Imperfect Information Games Using CFR+](https://mlsanity.com/paper/1407.5042/) (2014)
- [Solving Imperfect-Information Games via Discounted Regret Minimization](https://mlsanity.com/paper/1809.04040/) (2018)
- [Faster Game Solving via Predictive Blackwell Approachability: Connecting Regret Matching and Mirror Descent](https://mlsanity.com/paper/2007.14358/) (2020)
- [Rethinking Formal Models of Partially Observable Multiagent Decision Making](https://mlsanity.com/paper/1906.11110/) (2019)
- [Graphical Models for Game Theory](https://mlsanity.com/paper/1301.2281/) (2013)
- [Regret Matching+: (In)Stability and Fast Convergence in Games](https://mlsanity.com/paper/2305.14709/) (2023)
- [Faster Game Solving via Hyperparameter Schedules](https://mlsanity.com/paper/2404.09097/) (2024)
- [A Polynomial-Time Algorithm for 1/3-Approximate Nash Equilibria in Bimatrix Games](https://mlsanity.com/paper/2204.11525/) (2022)
- [Guarantees for Self-Play in Multiplayer Games via Polymatrix Decomposability](https://mlsanity.com/paper/2310.11518/) (2023)

## Built on by (most cited first, 1 of 1)

- [Governing Strategic Dynamics: Equilibrium Stabilization via Divergence-Driven Control](https://mlsanity.com/paper/2506.23734/) (2025)

## Abstract (arXiv)

Nash equilibrium is perhaps the best-known solution concept in game theory. Such a solution assigns a strategy to each player which offers no incentive to unilaterally deviate. While a Nash equilibrium is guaranteed to always exist, the problem of finding one in general-sum games is PPAD-complete, generally considered intractable. Regret minimization is an efficient framework for approximating Nash equilibria in two-player zero-sum games. However, in general-sum games, such algorithms are only guaranteed to converge to a coarse-correlated equilibrium (CCE), a solution concept where players can correlate their strategies. In this work, we use meta-learning to minimize the correlations in strategies produced by a regret minimizer. This encourages the regret minimizer to find strategies that are closer to a Nash equilibrium. The meta-learned regret minimizer is still guaranteed to converge to a CCE, but we give a bound on the distance to Nash equilibrium in terms of our meta-loss. We evaluate our approach in general-sum imperfect information games. Our algorithms provide significantly better approximations of Nash equilibria than state-of-the-art regret minimization techniques.