2 papers
cs.AI2020
Policy Gradient Reinforcement Learning for Policy Represented by Fuzzy Rules: Application to Simulations of Speed Control of an Automobile
Seiji Ishihara, Harukazu Igarashi
A method of a fusion of fuzzy inference and policy gradient reinforcement learning has been proposed that directly learns, as maximizes the expected value of the reward per episode…
cs.AI2019
Learning Position Evaluation Functions Used in Monte Carlo Softmax Search
Harukazu Igarashi, Yuichi Morioka, Kazumasa Yamamoto
This paper makes two proposals for Monte Carlo Softmax Search, which is a recently proposed method that is classified as a selective search like the Monte Carlo Tree Search. The fi…