首个基于强化学习的印度纸牌游戏智能体，速度快过传统算法7000倍。
IRumAI: Reinforcement Learning for Indian Rummy
- 用PPO+双分支卷积网络，结合摸牌信息与弃牌反馈进行训练。
- 对最强对手胜率达53.9%，推理速度仅0.33毫秒/步。
- 无需显式搜索，适合实时对抗场景，可隐式推断对手手牌。
尽管印度纸牌游戏拥有庞大的玩家基础和复杂的隐藏信息机制，但尚未有强化学习研究关注该领域。现有智能体依赖组合搜索，战术强但推理慢。我们提出IRumAI，首个针对该领域的强化学习智能体。它融合近端策略优化（PPO）、融牌感知的观察编码、死牌驱动的奖励设计及双分支卷积架构。IRumAI仅通过弱启发式策略进行强化学习训练，并在更强演示数据上进行一次行为克隆预热。其泛化能力优异，击败整个基线层级，包括在未参与训练的最强搜索型对手上实现53.9%胜率。相比状态最先进启发式方法，其单步推理时间仅0.33毫秒，提速超7000倍。消融实验验证了架构设计的有效性，线性探测显示网络能从公开互动中隐式建模对手隐藏手牌。
原文摘要 · Abstract (English)
Despite its massive player base and complex hidden-information dynamics, Indian Rummy has received no reinforcement learning attention. Existing agents rely on combinatorial search, which is tactically strong but slow at inference. We present IRumAI, the first RL agent for the domain. IRumAI integrates Proximal Policy Optimization (PPO), meld-aware observation encoding, deadwood-driven reward shaping, and a dual-branch convolutional architecture. IRumAI is RL-trained solely against weak heuristics, after a one-time behaviour-cloning warm-start on stronger demonstration data. It generalises to defeat the entire baseline hierarchy, including a 53.9% win rate against the strongest search-based opponent unseen during RL training. Bypassing explicit search, IRumAI requires just 0.33 ms per action, which is over 7,000x faster than the state-of-the-art heuristic. Ablations validate our architectural choices, and linear probing reveals that the network implicitly models the opponent's hidden hand from public interactions.
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