2025/05/31 by Nina Cohen, Kordel K. France, Cohen, Nina +1
Engineering · #Artificial Intelligence (cs.AI) #Computer Science and Game Theory (cs.GT) #Elevator Systems and Control #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA)
paper · pdf · doi:10.48550/arxiv.2506.00458
openalex publication_date 2025/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.