Authors:
Dhouha Ben Noureddine
1
;
Atef Gharbi
2
and
Samir Ben Ahmed
3
Affiliations:
1
LISI, National Institute of Applied Science and Technology, INSAT, University of Carthage, FST and University of El Manar, Tunisia
;
2
LISI, National Institute of Applied Science and Technology, INSAT and University of Carthage, Tunisia
;
3
FST and University of El Manar, Tunisia
Keyword(s):
Task Allocation, Multi-agent System, Deep Reinforcement Learning, Communication, Distributed Environment.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
The task allocation problem in a distributed environment is one of the most challenging problems in a multiagent
system. We propose a new task allocation process using deep reinforcement learning that allows cooperating
agents to act automatically and learn how to communicate with other neighboring agents to allocate tasks
and share resources. Through learning capabilities, agents will be able to reason conveniently, generate an appropriate
policy and make a good decision. Our experiments show that it is possible to allocate tasks using
deep Q-learning and more importantly show the performance of our distributed task allocation approach.