# Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess

arXiv: https://arxiv.org/abs/2110.01810 · HTML: https://mlsanity.com/paper/2110.01810/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 67 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018)
- [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://mlsanity.com/paper/1807.03247/) (2018)
- [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)
- [XRAI: Better Attributions Through Regions](https://mlsanity.com/paper/1906.02825/) (2019)
- [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)
- [On the Limitations of Representing Functions on Sets](https://mlsanity.com/paper/1901.09006/) (2019)
- [Algorithms for multi-armed bandit problems](https://mlsanity.com/paper/1402.6028/) (2014)
- [Accelerating Self-Play Learning in Go](https://mlsanity.com/paper/1902.10565/) (2019)
- [ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero](https://mlsanity.com/paper/1902.04522/) (2019)
- [Corralling Stochastic Bandit Algorithms](https://mlsanity.com/paper/2006.09255/) (2020)
- [DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs](https://mlsanity.com/paper/1909.13003/) (2019)
- [To Balance or Not to Balance: A Simple-yet-Effective Approach for Learning with Long-Tailed Distributions](https://mlsanity.com/paper/1912.04486/) (2019)
- [Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess](https://mlsanity.com/paper/2009.04374/) (2020)
- [Measuring and Harnessing Transference in Multi-Task Learning](https://mlsanity.com/paper/2010.15413/) (2020)
- [Computing Approximate Nash Equilibria and Robust Best-Responses Using Sampling](https://mlsanity.com/paper/1401.4591/) (2014)
- [On the Complexity of Reconnaissance Blind Chess](https://mlsanity.com/paper/1811.03119/) (2018)

## Built on by (most cited first, 4 of 4)

- [Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess](https://mlsanity.com/paper/2208.02029/) (2022)
- [Towards Using Fully Observable Policies for POMDPs](https://mlsanity.com/paper/2207.11737/) (2022)
- [Neural Network-based Information Set Weighting for Playing Reconnaissance Blind Chess](https://mlsanity.com/paper/2407.05864/) (2024)
- [Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem](https://mlsanity.com/paper/2505.08451/) (2025)

## Abstract (arXiv)

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.