AI RESEARCH
From Imitation to Interaction: Mastering Game of Schnapsen with Shallow Reinforcement Learning
arXiv CS.LG
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ArXi:2605.17162v1 Announce Type: cross This paper investigates whether shallow neural network agents can master the card game Schnapsen and challenge a strong search-based baseline, RdeepBot, which uses Monte Carlo sampling and lookahead search. Guided by a progressively complex experimental design, we first evaluate a supervised learning agent (MLPBot) trained on replay data and then a reinforcement learning agent (RLBot) with the same shallow architecture trained through asynchronous Monte Carlo updates and experience replay.