期刊
NEURAL NETWORKS
卷 15, 期 4-6, 页码 665-687出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0893-6080(02)00056-4
关键词
reinforcement learning, exploitation-exploration problem; neuromodulator; attention; partially observable Markov decision process
In reinforcement learning (RL), the duality between exploitation and exploration has long been an important issue. This paper presents a new method that controls the balance between exploitation and exploration. Our learning scheme is based on model-based RL, in which the Bayes inference with forgetting effect estimates the state-transition probability of the environment. The balance parameter, which corresponds to the randomness in action selection, is controlled based on variation of action results and perception of environmental change. When applied to maze tasks, our method successfully obtains good controls by adapting to environmental changes. Recently, Usher et al. [Science 283 (1999) 549] has suggested that noradrenergic neurons in the locus coeruleus may control the exploitation-exploration balance in a real brain and that the balance may correspond to the level of animal's selective attention. According to this scenario, we also discuss a possible implementation in the brain. (C) 2002 Elsevier Science Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据