# LiteEFG: An Efficient Python Library for Solving Extensive-form Games

arXiv: https://arxiv.org/abs/2407.20351 · HTML: https://mlsanity.com/paper/2407.20351/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [The Hanabi Challenge: A New Frontier for AI Research](https://mlsanity.com/paper/1902.00506/) (2019)
- [OpenSpiel: A Framework for Reinforcement Learning in Games](https://mlsanity.com/paper/1908.09453/) (2019)
- [Solving Imperfect-Information Games via Discounted Regret Minimization](https://mlsanity.com/paper/1809.04040/) (2018)
- [A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games](https://mlsanity.com/paper/2206.05825/) (2022)
- [Faster Game Solving via Predictive Blackwell Approachability: Connecting Regret Matching and Mirror Descent](https://mlsanity.com/paper/2007.14358/) (2020)
- [Last-iterate Convergence in Extensive-Form Games](https://mlsanity.com/paper/2106.14326/) (2021)
- [The Power of Regularization in Solving Extensive-Form Games](https://mlsanity.com/paper/2206.09495/) (2022)
- [Alternating Mirror Descent for Constrained Min-Max Games](https://mlsanity.com/paper/2206.04160/) (2022)

## Built on by (most cited first, 3 of 3)

- [Scale-Invariant Regret Matching and Online Learning with Optimal Convergence: Bridging Theory and Practice in Zero-Sum Games](https://mlsanity.com/paper/2510.04407/) (2025)
- [Game of Thought: Robust Information Seeking with Large Language Models Using Game Theory](https://mlsanity.com/paper/2602.01708/) (2026)
- [Decision Making under Imperfect Recall: Algorithms and Benchmarks](https://mlsanity.com/paper/2602.15252/) (2026)

## Abstract (arXiv)

LiteEFG is an efficient library with easy-to-use Python bindings, which can solve multiplayer extensive-form games (EFGs). LiteEFG enables the user to express computation graphs in Python to define updates on the game tree structure. The graph is then executed by the C++ backend, leading to significant speedups compared to running the algorithm in Python. Moreover, in LiteEFG, the user needs to only specify the computation graph of the update rule in a decision node of the game, and LiteEFG will automatically distribute the update rule to each decision node and handle the structure of the imperfect-information game.