Title :
Parallel and distributed multi-agent reinforcement learning
Author :
Kaya, Mehmet ; Arslan, Ahmet
Author_Institution :
Dept. of Comput. Eng., Firat Univ., Elazig, Turkey
Abstract :
The application of parallel and distributed systems to multi-agent environments has attracted recent attention. Multi-agent systems are a particular type of distributed artificial intelligence system. This paper presents an approach to learning in parallel and distributed systems. A variant of the job assignment problem is chosen as an evaluation task. This is an NP-hard problem, which is relevant to many industrial application domains. Experimental results show the effectiveness of the proposed approach
Keywords :
computational complexity; distributed processing; learning (artificial intelligence); multi-agent systems; production control; scheduling; NP-hard problem; distributed artificial intelligence; distributed systems; industrial applications; job assignment problem; multi-agent reinforcement learning; parallel systems; Application software; Artificial intelligence; Concurrent computing; Control systems; Distributed computing; Intelligent robots; Learning; Multiagent systems; Parallel processing; Robustness;
Conference_Titel :
Parallel and Distributed Systems, 2001. ICPADS 2001. Proceedings. Eighth International Conference on
Conference_Location :
Kyongju City
Print_ISBN :
0-7695-1153-8
DOI :
10.1109/ICPADS.2001.934851
