From Imitation to Interaction: Mastering Game of Schnapsen with Shallow Reinforcement Learning

In three linesShallow neural network agents master the card game Schnapsen through reinforcement learning. RLBot, trained via asynchronous Monte Carlo updates, outperforms MLPBot (supervised imitation) and achieves statistically significant wins against RdeepBot, a search-based baseline. Combining learned value functions with deeper lookahead during gameplay improves performance.

Summary generated by Claude — human-verified