用强化学习训练纸牌游戏汉诺比的协作智能体，发现深度Q网络表现最优。
Reinforcement Learning for Hanabi
- 采用深度Q学习和时序差分算法训练协作智能体
- 深度Q学习在多数对手下得分最高，表现最稳定
- 揭示不同智能体间的互动策略，适合多智能体协作研究
汉诺比作为合作类卡牌游戏，因其环境不完全可观测性成为强化学习研究的重要场景。本文对比了多种表格型与深度强化学习算法在对抗同类及异类智能体时的表现。结果表明，某些智能体在特定对手下表现最佳，而部分智能体通过适应对手行为实现平均得分更高。时序差分（TD）算法整体性能优于表格型方法，其中表格型期望SARSA与深度Q学习智能体表现最优。研究还量化了各算法的优势条件，揭示了不同类型智能体间的有趣交互关系。
原文摘要 · Abstract (English)
Hanabi has become a popular game for research when it comes to reinforcement learning (RL) as it is one of the few cooperative card games where you have incomplete knowledge of the entire environment, thus presenting a challenge for a RL agent. We explored different tabular and deep reinforcement learning algorithms to see which had the best performance both against an agent of the same type and also against other types of agents. We establish that certain agents played their highest scoring games against specific agents while others exhibited higher scores on average by adapting to the opposing agent's behavior. We attempted to quantify the conditions under which each algorithm provides the best advantage and identified the most interesting interactions between agents of different types. In the end, we found that temporal difference (TD) algorithms had better overall performance and balancing of play types compared to tabular agents. Specifically, tabular Expected SARSA and deep Q-Learning agents showed the best performance.
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