TY - GEN
T1 - Multi-agent reinforcement learning system integrating exploitation- and exploration-oriented learning
AU - Kurihara, Satoshi
AU - Sugawara, Toshiharu
AU - Onai, Rikio
PY - 1998/1/1
Y1 - 1998/1/1
N2 - This paper proposes and evaluates MarLee, a multi-agent reinforcement learning system that integrates both exploitation- and exploration-oriented learning. Compared with conventional reinforcement learnings, MarLee is more robust in the face of a dynamically changing environment and is able to perform exploration-oriented learning efficiently even in a large-scale environment. Thus, MarLee is well suited for autonomous systems, for example, software agents and mobile robots, that operate in dynamic, large-scale environments, like the real-world and the Internet. Spreading activation, based on the behavior-based approach, is used to explore the environment, so by manipulating the parameters of the spreading activation, it is easy to tune the learning characteristics. The fundamental effectiveness of MarLee was demonstrated by simulation.
AB - This paper proposes and evaluates MarLee, a multi-agent reinforcement learning system that integrates both exploitation- and exploration-oriented learning. Compared with conventional reinforcement learnings, MarLee is more robust in the face of a dynamically changing environment and is able to perform exploration-oriented learning efficiently even in a large-scale environment. Thus, MarLee is well suited for autonomous systems, for example, software agents and mobile robots, that operate in dynamic, large-scale environments, like the real-world and the Internet. Spreading activation, based on the behavior-based approach, is used to explore the environment, so by manipulating the parameters of the spreading activation, it is easy to tune the learning characteristics. The fundamental effectiveness of MarLee was demonstrated by simulation.
KW - Dynamic environment
KW - Exploitation-oriented
KW - Exploration-oriented
KW - Multi-agent reinforcement learning
KW - Spreading activation
UR - http://www.scopus.com/inward/record.url?scp=84961359588&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961359588&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84961359588
SN - 3540654771
SN - 9783540654773
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 45
EP - 57
BT - Multi-Agent Systems
PB - Springer Verlag
T2 - 4th Australian Workshop on Distributed Artificial Intelligence, DAK 1998
Y2 - 13 July 1998 through 13 July 1998
ER -