# Neural Network-based Information Set Weighting for Playing Reconnaissance Blind Chess

arXiv: https://arxiv.org/abs/2407.05864 · HTML: https://mlsanity.com/paper/2407.05864/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker](https://mlsanity.com/paper/1701.01724/) (2017)
- [Deep Reinforcement Learning from Self-Play in Imperfect-Information Games](https://mlsanity.com/paper/1603.01121/) (2016)
- [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)
- [DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess](https://mlsanity.com/paper/1711.09667/) (2017)
- [Mastering Strategy Card Game (Hearthstone) with Improved Techniques](https://mlsanity.com/paper/2303.05197/) (2023)
- [Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking](https://mlsanity.com/paper/2105.11864/) (2021)
- [Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess](https://mlsanity.com/paper/2110.01810/) (2021)
- [Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess](https://mlsanity.com/paper/2208.02029/) (2022)

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

- [MAPLE: Multi-State Aggregated Policy Evaluation for AlphaZero in Imperfect-Information Games](https://mlsanity.com/paper/2605.24139/) (2026)

## Abstract (arXiv)

In imperfect information games, the game state is generally not fully observable to players. Therefore, good gameplay requires policies that deal with the different information that is hidden from each player. To combat this, effective algorithms often reason about information sets; the sets of all possible game states that are consistent with a player's observations. While there is no way to distinguish between the states within an information set, this property does not imply that all states are equally likely to occur in play. We extend previous research on assigning weights to the states in an information set in order to facilitate better gameplay in the imperfect information game of Reconnaissance Blind Chess. For this, we train two different neural networks which estimate the likelihood of each state in an information set from historical game data. Experimentally, we find that a Siamese neural network is able to achieve higher accuracy and is more efficient than a classical convolutional neural network for the given domain. Finally, we evaluate an RBC-playing agent that is based on the generated weightings and compare different parameter settings that influence how strongly it should rely on them. The resulting best player is ranked 5th on the public leaderboard.