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This paper focuses on  generalization  in reinforcement learning from the  time horizon  viewpoint, exploring the method that generalizes multiple Q-tables in the multiagent reinforcement  learning domain. For this purpose, we propose  time horizon generalization  for reinforcement  learning, which consists of (1) Q-table selection method and (2) Q-table merge timing method,  enabling agents to (1) select which Q-tables can be generalized from among many Q-tables and (2)  determine when the selected Q-tables should be generalized. Intensive simulation on the bargaining  game as sequential interaction game have revealed the following implications: (1) both Q-table  selection and merging timing methods help replicate the subject experimental results without  ad-hoc parameter setting; and (2) such replication succeeds by agents using the proposed methods  with smaller numbers of Q-tables.

Time Horizon Generalization in Reinforcement Learning: Generalizing Multiple Q-Tables in Q-Learning Agents