[3Win5] Poster session 3

[3Win5-06] A Comparative Study of Satisficing in Deep Reinforcement Learning

〇Yuna Kikuchi¹, Tatsuzi Takahashi², Yu Kono² (1.Graduate School of Tokyo Denki University, 2.School of Science and Engineering, Tokyo Denki University)

Keywords:Reinforcement Learning , Cognitive Science, Machine Learning

Humans have a tendency to aim at achieving a certain goal level rather than maximizing gains single-mindedly. This tendency is clearly distinct from optimization, and corresponds to the concept of “satisficing,” which is to select a sufficiently good option drawing on a limited amount of information. Although RS is a discrete action algorithm , it is applicable to deep reinforcement learning, and when the target level is clear it outperforms optimization algorithms. However, a comparison of the performance of RS with that of optimization algorithms, within the framework of deep reinforcement learning, has not been conducted. Since satisfication itself is a very simple concept, there could be countless ways to implement it. In this study, we adopted a widely used satisficing algorithm as a baseline for exploration in a deep reinforcement learning toy task, compared its performance with that of RS, and showed that the latter outperforms the former. In turn, this result reinforces the validity of the notion of subjective regret, which is a concept of RS that is simple but effective in multiple ways.