出版社：The Japanese Society for Artificial Intelligence
摘要：This paper describes optimization of the betting fraction parameter in compound reinforcement learning. Compound reinforcement learning maximizes the expected logarithm of compound returns in return-based MDPs. However, a new betting fraction parameter is introduced in order not to diverge values to negative infinity and it causes a problem of choosing the parameter. In this paper, we proposed a method to optimize the betting fraction with on-line gradient ascent in compound reinforcement learning.
