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

Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess