stochastic

Building smarter AI for puzzle games with previewed chance and stack constraints

Planning an AI around previewed chance events and long-horizon throughput is a sharp problem, and the trade-offs are framed clearly. Separating deterministic afterstates from explicit chance nodes, paired with a policy/value network and PUCT, feels like the right structural instinct, especially given the preview-conditioned fourth action. Their honest reporting on what failed, like Q-head calibration and exhaustive leaf maximization, is more useful than most success stories.