Behavior acquisition based on multi-module learning system in multi-agent environment

被引：0

作者：

Takahashi, Y ^{[1
]}

Edazawa, K ^{[1
]}

Asada, M ^{[1
]}

机构：

[1] Osaka Univ, Grad Sch Engn, Emergent Robot Area, Dept Adapt Machine Syst, Suita, Osaka 5650871, Japan

来源：

ROBOCUP 2002: ROBOT SOCCER WORLD CUP VI | 2003年 / 2752卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The conventional reinforcement learning approaches have difficulties to handle the policy alternation of the opponents because it may cause dynamic changes of state transition probabilities of which stability is necessary for the learning to converge. This paper presents a method of multi-module reinforcement learning in a multiagent environment, by which the learning agent can adapt itself to the policy changes of the opponents. We show a preliminary result of a simple soccer situation in the context of RoboCup.

引用

下载

页码：435 / 442

页数：8

共 50 条

[1] Multi-module learning system for behavior acquisition in multi-agent environment
Takahashi, Y
Edazawa, K
Asada, M
2002 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-3, PROCEEDINGS, 2002, : 927 - 931
[2] Modular learning system and scheduling for behavior acquisition in multi-agent environment
Takahashi, Y
Edazawa, K
Asada, M
ROBOCUP 2004: ROBOT SOCCER WORLD CUP VIII, 2005, 3276 : 548 - 555
[3] Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
He, Xu
An, Bo
Li, Yanghua
Chen, Haikai
Wang, Rundong
Wang, Xinrun
Yu, Runsheng
Li, Xin
Wang, Zhirong
RECSYS 2020: 14TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, 2020, : 210 - 219
[4] Cooperative behavior acquisition based modular Q learning in multi-agent system
Zhou, T
Hong, BR
Shi, CX
Zhou, HY
PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, 2005, : 205 - 210
[5] Behavior modeling based on multi-agent and multi-agent simulation environment
Yin, QJ
Du, XY
Huang, K
SYSTEM SIMULATION AND SCIENTIFIC COMPUTING, VOLS 1 AND 2, PROCEEDINGS, 2005, : 1531 - 1536
[6] Fast reinforcement learning approach to cooperative behavior acquisition in multi-agent system
Piao, S
Hong, B
2002 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-3, PROCEEDINGS, 2002, : 871 - 875
[7] Constructing adaptive individual learning environment based on multi-agent system
Chen, Peng
Meng, Anbo
Zhao, Chunhua
CIS WORKSHOPS 2007: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY WORKSHOPS, 2007, : 374 - +
[8] Representation of the Perceived Environment and Acquisition of Behavior Rule for Multi-Agent Systems by Q-Learning
Xie, Mengchun
PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOTS AND AGENTS, 2009, : 398 - 402
[9] Cooperative Behavior Acquisition in Multi-agent Reinforcement Learning System Using Attention Degree
Kobayashi, Kunikazu
Kurano, Tadashi
Kuremoto, Takashi
Obayashi, Masanao
NEURAL INFORMATION PROCESSING, ICONIP 2012, PT III, 2012, 7665 : 537 - 544
[10] The acquisition of sociality by using Q-learning in a multi-agent environment
Nagayuki, Yasuo
PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 16TH '11), 2011, : 820 - 823

← 1 2 3 4 5 →