Authors:
Wiem Zemzem
and
Moncef Tagina
Affiliation:
COSMOS Laboratory, National School of Computer Science and University of Manouba, Tunisia
Keyword(s):
Distributed Reinforcement Learning, A Cooperative Action Selection Strategy, A Relay Agent, Unknown and Stationary Environments.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Intelligent Agents
;
Internet Technology
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Software Engineering
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
This paper focuses on distributed reinforcement learning in cooperative multi-agent systems, where several simultaneously and independently acting agents have to perform a common foraging task. To do that, a novel cooperative action selection strategy and a new kind of agents, called "relay agent", are proposed. The conducted simulation tests indicate that our proposals improve coordination between learners and are extremely efficient in terms of cooperation in large, unknown and stationary environments.