A Gold-Standard Study of What Makes a Lightweight Game-Playing Agent Strong
Reinforcement learning agents for imperfect-information card games are only as strong as the opponents they train against, and they are hard to grade, since they beat a random opponent over 99 percent of the time and only tie copies of themselves. So we build a strong, fixed, rule-based expert for Gin Rummy and use it only as a yardstick, never for training. It beats every agent we trained 70 to 99 percent of the time. Across more than a hundred runs, we isolate what makes a lightweight agent stronger. Trust region updates, a well-aimed reward, a curriculum of tougher opponents, warm starting, and keeping the best checkpoint all help, and stacking them lifts a self-play champion from abou...
| Authors | Nima Kelidari, Mohammadsaeed Haghi, Mahdi Salmani |
|---|---|
| Primary category | cs.LG |
| Published | 7 July 2026 |
| Last updated at source | 29 August 2026 |
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