gin-rummy

This paper investigates design choices for training lightweight reinforcement learning agents in imperfect-information card games, using a fixed rule-based expert as a yardstick for Gin Rummy and Leduc Hold'em. It finds that trust region updates, careful reward design, curriculum learning, warm starting, and checkpoint retention improve performance, while several common techniques like reward shaping and large language model opponents do not help.