Elevator Group Control Using Multiple Reinforcement Learning Agents

Machine Learning

Volumn 12, Issue 4, 1998, Pages 235-262

  Crites, Robert H ^a   Barto, Andrew G ^b  

^a Unica Technologies   (United States)

^b University of Massachusetts   (United States)

Author keywords

Discrete event dynamic systems; Elevator group control; Multiple agents; Reinforcement learning; Teams

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DISCRETE TIME CONTROL SYSTEMS; DYNAMIC PROGRAMMING; ELEVATORS; LEARNING ALGORITHMS; PROBLEM SOLVING; RANDOM PROCESSES;

DISCRETE EVENT DYNAMIC SYSTEMS; ELEVATOR GROUP CONTROL; REINFORCEMENT LEARNING (RL);

LEARNING SYSTEMS;

EID: 0032208335     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/a:1007518724497     Document Type: Article

References (43)

1. Axelrod, R.M. (1984). The Evolution of Cooperation. New York, NY: Basic Books.
2. Bao, G., Cassandras, G.G., Djaferis, T.E., Gandhi, A.D., & Looze, D.P. (1994). Elevator dispatchers for down peak traffic. ECE Department Technical Report, University of Massachusetts.
3. Barto, A.G. (1989). From chemotaxis to cooperativity: Abstract exercises in neuronal learning strategies. In R. Durbin, C. Miall, and G. Mitchison, (Eds.), The Computing Neuron. Wokingham, England: Addison-Wesley.
4. Barto, A.G., Bradtke, S.J., & Singh, S.P. (1995). Learning to act using real-time dynamic programming. Artificial Intelligence, 72, 81-138.
5. Bertsekas, D.P. & Tsitsiklis, J.N. (1996). Neuro-Dynamic Programming. Belmont, MA: Athena Scientific Press.
6. Bradtke, S.J. (1993). Distributed adaptive optimal control of flexible structures. Unpublished manuscript.
7. Bradtke, S.J. & Duff, M. O. (1995). Reinforcement learning methods for continuous-time Markov decision problems. In G. Tesauro, D. Touretzky, and T. Leen, (Eds.), Advances in Neural Information Processing Systems 7. Cambridge, MA: MIT Press.
8. Cassandras, C.G. (1993). Discrete Event Systems: Modeling and Performance Analysis. Homewood, IL: Aksen Associates.
9. Crites, R.H. (1996). Large-Scale Dynamic Optimization Using Teams of Reinforcement Learning Agents. PhD thesis, University of Massachusetts.
10. Crites, R.H. & Barto, A.G. (1996). Forming control policies from simulation models using reinforcement learning. Proceedings of the Ninth Yale Workshop on Adaptive and Learning Systems.
11. Crites, R. H. & Barto, A.G. (1996). Improving elevator performance using reinforcement learning. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, (Eds.), Advances in Neural Information Processing Systems 8. Cambridge, MA: MIT Press.
12. Dayan, P. & Hinton, G.E. (1993). Feudal reinforcement learning. In S. J. Hanson, J. D. Cowan, and C. L. Giles, (Eds.), Advances in Neural Information Processing Systems 5. San Mateo, CA: Morgan Kaufmann.
13. Fujino, A., Tobita, T., & Yoneda, K. (1992). An on-line tuning method for multi-objective control of elevator group. Proceedings of the International Conference on Industrial Electronics, Control, Instrumentation, and Automation, (pp. 795-800).
14. Imasaki, N., Kiji, J., & Endo, T. (1992). A fuzzy neural network and its application to elevator group control. In T. Terano, M. Sugeno, M. Mukaidono, and K. Shigemasu, (Eds.), Fuzzy Engineering Toward Human Friendly Systems. Amsterdam: IOS Press.
15. Levy, D., Yadin, M., & Alexandrovitz, A. (1977). Optimal control of elevators. International Journal of Systems Science, 8, 301-320.
16. Lewis, J. (1991). A Dynamic Load Balancing Approach to the Control of Multiserver Polling Systems with Applications to Elevator System Dispatching. PhD thesis, ECE department, University of Massachusetts.
17. Littman, M. & Boyan, J. (1993). A distributed reinforcement learning scheme for network routing. Technical Report CMU-CS-93-165, Carnegie Mellon University.
18. Littman, M.L. (1994). Markov games as a framework for multi-agent reinforcement learning. Proceedings of the Eleventh International Conference on Machine Learning. San Mateo, CA: Morgan Kaufmann.
19. Littman, M.L. (1996). Algorithms for Sequential Decision Making. PhD thesis, Brown University.
20. Markey, K.L. (1994). Efficient learning of multiple degree-of-freedom control problems with quasi-independent Q-agents. In M. C. Mozer, P. Smolensky, D. S. Touretzky, J. L. Elman, and A. S. Weigend, (Eds.), Proceedings of the 1993 Connectionist Models Summer School. Hillsdale, NJ: Erlbaum Associates.
21. Markon, S., Kita, H., & Nishikawa, Y. (1994). Adaptive optimal elevator group control by use of neural networks. Transactions of the Institute of Systems, Control, and Information Engineers, 7, 487-497.
22. Narendra, K.S. & Thathachar, M.A.L. (1989). Learning Automata: An Introduction. Englewood Cliffs, NJ: Prentice-Hall.
23. Ovaska, S.J. (1992). Electronics and information technology in high-range elevator systems. Mechatronics, 2, 89-99.
24. Pepyne, D.L. & Cassandras, C.G. (1997). Optimal dispatching control for elevator systems during uppeak traffic. IEEE Transactions on Control Systems Technology, 5, 629-643.
25. Rumelhart, D.E., McClelland, J.L., & the PDP Research Group. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press.
26. Sakai, Y & Kurosawa, K. (1984). Development of elevator supervisory group control system with artificial intelligence. Hitachi Review, 33, 25-30.
27. Samuel, A.L. (1963). Some studies in machine learning using the game of checkers. In E. Feigenbaum and J. Feldman, (Eds.), Computers and Thought. New York, NY: McGraw-Hill.
28. Sandholm, T.W. & Crites, R.H. (1996). Multiagent reinforcement learning in the iterated prisoner's dilemma. Biosystems, 37, 147-166.
29. Shoham, Y. & Tennenholtz, M. (1993). Co-learning and the evolution of coordinated multi-agent activity.
30. Siikonen, M.L. (1993). Elevator traffic simulation. Simulation, 61, 257-267.
31. Strakosch, G.R. (1983). Vertical Transportation: Elevators and Escalators. New York, NY: Wiley and Sons.
32. Sutton, R.S. & Barto, A.G. (1998). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.
33. Tan, M. (1993). Multi-agent reinforcement learning: Independent vs. cooperative agents. Proceedings of the Tenth International Conference on Machine Learning.
34. Tesauro, G. (1992). Practical issues in temporal difference learning. Machine Learning, 8, 257-277.
35. Tesauro, G. (1994). TD-Gammon, a self-teaching backgammon program, achieves master-level play. Neural Computation, 6, 215-219.
36. Tesauro, G. (1995). Temporal difference learning and TD-Gammon. Communications of the ACM, 38, 58-68.
37. Tobita, T., Fujino, A., Inaba, H., Yoneda, K., & Ueshima, T. (1991). An elevator characterized group supervisory control system. Proceedings of IECON, (pp. 1972-1976).
38. Tsetlin, M. L. (1973). Automaton Theory and Modeling of Biological Systems. New York, NY: Academic Press.
39. Ujihara, H. & Amano, M. (1994). The latest elevator group-control system. Mitsubishi Electric Advance, 67, 10-12.
40. Ujihara, H. & Tsuji, S. (1988). The revolutionary AI-2100 elevator-group control system and the new intelligent option series. Mitsubishi Electric Advance, 45, 5-8.
41. Watkins, C. J. C. H. (1989). Learning from Delayed Rewards. PhD thesis, Cambridge University.
42. Weiss, G. & Sen, S. (1996). Adaptation and Learning in Multi-Agent Systems. Lecture Notes in Artificial Intelligence, Volume 1042. Berlin: Springer Verlag.
43. Widrow, B. & Stearns, S.D. (1985). Adaptive Signal Processing. Englewood Cliffs, NJ: Prentice-Hall.