title: Parallel reinforcement learning for weighted multi-criteria model with adaptive margin.
abstract: Reinforcement learning (RL) for a linear family of tasks is described in this paper. The key of our discussion is nonlinearity of the optimal solution even if the task family is linear; we cannot obtain the optimal policy using a naive approach. Although an algorithm exists for calculating the equivalent result to Q-learning for each task simultaneously, it presents the problem of explosion of set sizes. We therefore introduce adaptive margins to overcome this difficulty.
authors.affiliation: Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, Japan, hira@ics.saitama-u.ac.jp.
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Hiraoka
authors.forename: Manabu
authors.initials: M
authors.lastname: Yoshida
authors.forename: Taketoshi
authors.initials: T
authors.lastname: Mishima