[3R5-GS-2] Machine learning

[3R5-GS-2-05] Robustness of reliability to estimate the future in target-oriented reinforcement learning

〇Shuichi Arimura¹, Tatsuji Takahashi², Yu Kono² (1. Graduate School of Tokyo Denki University, 2. Tokyo Denki University)

Keywords:Reinforcement Learning

Human beings can achieve a balance between exploration and exploitation by setting an aspiration level, or a goal, and can efficiently learn a behavior sequence which satisfies the goal. Risk-sensitive Satisfying (RS) applies this decision-making tendency to search methods in reinforcement learning. This approach, however, does not work well in all of reinforcement learning settings, since RS does not handle action sequences well. On the other hand, a method is proposed that enables to learn reliability from action sequence. This method draws on experience memory, an approach in reinforcement learning , to compare the current state to the past, and dynamically calculate reliability. This method has followability in unsteady environments, and is expected to surpass the performance of existing methods. On the other hand, its performance has been verified only in a few tasks, still unknown as to its effectiveness in reinforcement learning tasks in general. This time, we verify and discuss this type of future-oriented reliability in various reinforcement learning tasks, and aim at its adaptation to reinforcement learning in general.