Using reinforcement learning for multi-policy optimization in decentralized autonomic systems - An experimental evaluation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5586 LNCS, Issue , 2009, Pages 105-119

  Dusparic, Ivana ^a   Cahill, Vinny ^a  

^a University of Limerick   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

AUTONOMIC SYSTEMS; DECENTRALIZED SYSTEM; EXPERIMENTAL EVALUATION; HETEROGENEOUS AGENTS; HIGH LEVEL POLICIES; LEARNING PROCESS; ON CURRENTS; POLICY OPTIMIZATION; REINFORCEMENT LEARNING TECHNIQUES; SELF-OPTIMIZATION; SINGLE STATE; URBAN TRAFFIC CONTROL; VEHICLE TYPES; WAITING TIME;

COMPUTER SCIENCE; EDUCATION; LEARNING ALGORITHMS; OPTIMIZATION; REINFORCEMENT; REINFORCEMENT LEARNING; TRAFFIC CONTROL;

MULTI AGENT SYSTEMS;

EID: 70350650156     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02704-8_9     Document Type: Conference Paper

References (22)

1. Abdulhai, B., Pringle, R., Karakoulas, G.: Reinforcement learning for the true adaptive traffic signal control. Journal of Transportation Engineering 129(3), 278-285 (2003)
2. Bazzan, A.L.: A distributed approach for coordination of traffic signal agents. Autonomous Agents and Multi-Agent Systems 10(1), 131-164 (2005)
3. Cuayáhuitl, H., Renals, S., Lemon, O., Shimodaira, H.: Learning multi-goal dialogue strategies using reinforcement learning with reduced state-action spaces. Int. Journal of Game Theory, 547-565 (2006)
4. Dowling, J.: The Decentralised Coordination of Self-Adaptive Components for Autonomic Distributed Systems. PhD thesis, Trinity College Dublin (2005)
5. He, L., Nort, N.: Hybrid genetic algorithms for telecommunications network backup routeing. BT Technology Journal 18(4) (October 2000)
6. Humphrys, M.: Action Selection methods using Reinforcement Learning. PhD thesis, University of Cambridge (1996)
7. Kadrovach, B.A., Lamont, G.B.: A particle swarm model for swarm-based networked sensor systems. In: SAC, pp. 918-924 (2002)
8. Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41-50 (2003)
9. Meignan, D., Simonin, O., Koukam, A.: Simulation and evaluation of urban bus-networks using a multiagent approach. Simulation Modelling Practice and Theory 15(6), 659-671 (2007)
10. Montresor, A., Meling, H., Babaoǧlu, Ö.: Messor: Load-balancing through a swarm of autonomous agents. In: Moro, G., Koubarakis, M. (eds.) AP2PC 2002. LNCS (LNAI), vol. 2530, pp. 125-137. Springer, Heidelberg (2003)
11. Natarajan, S., Tadepalli, P.: Dynamic preferences in multi-criteria reinforcement learning. In: ICML 2005: Proceedings of the 22nd international conference on Machine learning, pp. 601-608. ACM, New York (2005)
12. Oliveira, E., Duarte, N.: Making way for emergency vehicles. In: Proc. of the 2005 European Simulation and Modelling Conference, pp. 128-135 (2005)
13. Papageorgiou, M., Diakaki, C., Dinopoulou, V.: Review of road traffic control strategies. Proc. of the IEEE 91(12) (December 2003)
14. Pendrith, M.D.: Distributed reinforcement learning for a traffic engineering application. In: AGENTS 2000, pp. 404-411. ACM Press, New York (2000)
15. Reynolds, V., Cahill, V., Senart, A.: Requirements for an ubiquitous computing simulation and emulation environment. In: InterSense 2006. ACM Press, New York (2006)
16. Richter, S.: Learning traffic control - towards practical traffic control using policy gradients. Technical report, Albert-Ludwigs-Universität Freiburg (2006)
17. Russell, S., Norvig, P.: Aritifical Intelligence - A Modern Approach. Prentice-Hall, Englewood Cliffs (2003)
18. Salkham, A., Cunningham, R., Garg, A., Cahill, V.: A collaborative reinforcement learning approach to urban traffic control optimization. In: International Conference on Intelligent Agent Technology (December 2008)
19. Shelton, C.R.: Balancing multiple sources of reward in reinforcement learning. In: Neural Information Processing Systems 2000, pp. 1082-1088 (2000)
20. Suton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book. MIT Press, Cambridge (2002)
21. Tesauro, G., Chess, D.M., Walsh, W.E., Das, R., Segal, A., Whalley, I., Kephart, J.O., White, S.R.: A multi-agent systems approach to autonomic computing. In: AAMAS 2004, pp. 464-471 (2004)
22. Wiering, M.: Multi-agent reinforcement learning for traffic light control. In: Proc. of 17th Int. Conf. on Machine Learning, pp. 1151-1158. Morgan Kaufmann, San Francisco (2000)