Risk Sensitive Reinforcement Learning Scheme Is Suitable for Learning on a Budget

• Published in: Neural Information Processing pp. 202 - 210
• Main Authors: Kato, Kazuyoshi, Yamauchi, Koichiro
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Ineffective Kernel; Kernel Regression Model; Reinforcement Learning (RL); Resulting Value Function; Risk-sensitive RL
• ISBN: 3319466747; 9783319466743
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-46675-0_23

Risk-sensitive reinforcement learning (Risk-sensitiveRL) has been studied by many researchers. The methods are based on a prospect method, which imitates the value function of a human. Although they are mainly intended at imitating human behaviors, there are fewer discussions about the engineering meaning of it. In this paper, we show that Risk-sensitiveRL is useful for using online-learning machines whose resources are limited. In such a learning method, a part of the learned memories should be removed to create space for recording a new important instance. The experimental results show that risk-sensitive RL is superior to normal RL. This might mean that the human brain is also constructed by a limited number of neurons, so that humans hire the risk-sensitive value function for the learning.