How an adaptive learning rate benefits neuro-fuzzy reinforcement learning systems

被引：2

作者：

Kuremoto, Takashi ^{[1
]}

Obayashi, Masanao ^{[1
]}

Kobayashi, Kunikazu ^{[2
]}

Mabu, Shingo ^{[1
]}

机构：

[1] Graduate School of Science and Engineering, Yamaguchi University Tokiwadai 2-16-1, Ube,Yamaguchi

[2] Schoo of Information Science and Technology, Aichi Prefectural University Ibaragabasama 1522-3, Nagakute-Shi,Aichi

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8794卷

关键词：

Adaptive learning rate (ALR); Goal-exploration problem; Multi-agent system (MAS); Neuro-fuzzy system; Reinforcement learning (RL); Swarm behavior;

D O I：

10.1007/978-3-319-11857-4_37

中图分类号：

学科分类号：

摘要：

To acquire adaptive behaviors of multiple agents in the unknown environment, several neuro-fuzzy reinforcement learning systems (NFRLSs) have been proposed Kuremoto et al. Meanwhile, to manage the balance between exploration and exploitation in fuzzy reinforcement learning (FRL), an adaptive learning rate (ALR), which adjusting learning rate by considering “fuzzy visit value” of the current state, was proposed by Derhami et al. recently. In this paper, we intend to show how the ALR accelerates some NFRLSs which are reinforcement learning systems with a self-organizing fuzzy neural network (SOFNN) and different learning methods including actor-critic learning (ACL), and Sarsa learning (SL). Simulation results of goal-exploration problems showed the powerful effect of the ALR comparing with the conventional empirical fixed learning rates. © Springer International Publishing Switzerland 2014.

引用

页码：324 / 331

页数：7

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