# Mixture of Public and Private Distributions in Imperfect Information Games

arXiv: https://arxiv.org/abs/2405.14346 · HTML: https://mlsanity.com/paper/2405.14346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 9 of 9)

- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker](https://mlsanity.com/paper/1701.01724/) (2017)
- [OpenSpiel: A Framework for Reinforcement Learning in Games](https://mlsanity.com/paper/1908.09453/) (2019)
- [Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning](https://mlsanity.com/paper/2206.15378/) (2022)
- [Combining Deep Reinforcement Learning and Search for Imperfect-Information Games](https://mlsanity.com/paper/2007.13544/) (2020)
- [Rethinking Formal Models of Partially Observable Multiagent Decision Making](https://mlsanity.com/paper/1906.11110/) (2019)
- [The αμ Search Algorithm for the Game of Bridge](https://mlsanity.com/paper/1911.07960/) (2019)
- [Improving Search with Supervised Learning in Trick-Based Card Games](https://mlsanity.com/paper/1903.09604/) (2019)

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

- [Perfect Information Monte Carlo with Postponing Reasoning](https://mlsanity.com/paper/2408.02380/) (2024)

## Abstract (arXiv)

In imperfect information games (e.g. Bridge, Skat, Poker), one of the fundamental considerations is to infer the missing information while at the same time avoiding the disclosure of private information. Disregarding the issue of protecting private information can lead to a highly exploitable performance. Yet, excessive attention to it leads to hesitations that are no longer consistent with our private information. In our work, we show that to improve performance, one must choose whether to use a player's private information. We extend our work by proposing a new belief distribution depending on the amount of private and public information desired. We empirically demonstrate an increase in performance and, with the aim of further improving performance, the new distribution should be used according to the position in the game. Our experiments have been done on multiple benchmarks and in multiple determinization-based algorithms (PIMC and IS-MCTS).