Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess

Gregory Clark

NeurIPS 2021poster10 citations

Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess

Abstract

This paper introduces deep synoptic Monte Carlo planning (DSMCP) for large imperfect information games. The algorithm constructs a belief state with an unweighted particle filter and plans via playouts that start at samples drawn from the belief state. The algorithm accounts for uncertainty by performing inference on "synopses," a novel stochastic abstraction of information states. DSMCP is the basis of the program Penumbra, which won the official 2020 reconnaissance blind chess competition versus 33 other programs. This paper also evaluates algorithm variants that incorporate caution, paranoia, and a novel bandit algorithm. Furthermore, it audits the synopsis features used in Penumbra with per-bit saliency statistics.

reconnaissance blind chessimperfect informationdeep learningimitation learningplanninguncertainty

BibTeX

@inproceedings{
clark2021deep,
title={Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess},
author={Gregory Clark},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=Joy2imuk604}
}