  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.
