[1N3] [General Session] 2. Machine Learning

[1N3-03] Toward satisficing in general reinforcement learning

〇Kuniaki Satori¹, Yutaka Yoshida¹, Kenta Yamagishi¹, Yuya Ushida², Takumi Kamiya², Tatsuji Takahashi¹ (1. Tokyo Denki University, 2. Graduate School of Tokyo Denki Univerity)

Keywords:reinforcement learning, satisficing, bounded rationality

As the scope of reinforcement learning broadens, optimization becomes less realistic, and bounded rationality that considers the limitations in agents gets more important. Satisficing, the principal model of bounded rationality, models how people and animals explore and exploit. However, there is no efficient algorithm that represents satisficing can be applied to reinforcement learning in general. We apply our satisficing model, reference satisficing (RS) value function, and the global reference conversion (GRC) technique to the broader reinforcement learning tasks than in previous studies. In the three tasks we deal with in this study, RS and GRC work well, while there are some open problems for general reinforcement learning tasks.