Reinforcement Learning in the Multi-Robot Domain

Autonomous Robots

Volumn 4, Issue 1, 1997, Pages 73-83

  Matarić, Maja J ^a,b,c,d  

^a BRANDEIS UNIVERSITY   (United States)

^b UNIVERSITY OF KANSAS   (United States)

^c CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^d ATR   (Sweden)

Author keywords

Group behavior; Multi agent systems; Reinforcement learning; Robot learning; Robotics

Indexed keywords

ALGORITHMS; MOBILE ROBOTS; MOTION PLANNING; ROBOT PROGRAMMING; ROBOTICS; SENSORS;

GROUP BEHAVIOR; MULTI AGENT SYSTEMS; MULTI ROBOT DOMAIN; REINFORCEMENT LEARNING;

ROBOT LEARNING;

EID: 0030647149     PISSN: 09295593     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008819414322     Document Type: Article

References (29)

1. Asada, M., Uchibe, E., Noda, S., Tawaratsumida, S., and Hosoda, K. 1994. Coordination of multiple behaviors acquired by a avision-based reinforcement learning. In Proceedings IEEE/RSJ/GI International Conference on Intelligent Robots and Systems, Munich, Germany.
2. Atkeson, C.G. 1989. Using local models to control movement. In Proceedings, Neural Information Processing Systems Conference.
3. Atkeson, C.G., Aboaf, E.W., McIntyre, J., and Reinkensmeyer, D.J. 1988. Model-based robot learning. Technical Report AIM-1024, MIT.
4. Barto, A.G., Bradtke, S.J., and Singh, S.P. 1993. Learning to act using real-time dynamic programming. AI Journal.
5. Brooks, R.A. 1986. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, RA-2:14-23.
6. Brooks, R.A. 1990. The behavior language; user's guide. Technical Report AIM-1227, MIT Artificial Intelligence Lab.
7. Brooks, R.A. 1991. Intelligence without reason. In Proceedings, IJCAI-91.
8. Kaelbling, L.P. 1990. Learning in embedded systems, Ph.D. Thesis, Stanford University.
9. Lin, L.-J. 1991a. Programming robots using reinforcement learning and teaching. In Proceedings, AAAI-91, Pittsburgh, PA, pp. 781-786.
10. Lin, L.-J. 1991b. Self-improving reactive agents: Case studies of reinforcement learning frameworks. In From Animals to Animats: International Conference on Simulation of Adaptive Behavior, The MIT Press.
11. Maes, P. and Brooks, R.A. 1990. Learning to coordinate behaviors. In Proceedings, AAAI-91, Boston, MA, pp. 796-802.
12. Mahadevan, S. and Connell, J. 1990. Automatic programming of behavior-based robots using reinforcement learning. Technical report, IBM T.J. Watson Research Center Research Report.
13. Mahadevan, S. and Connell, J. 1991a. Automatic programming of behavior-based robots using reinforcement learning. In Proceedings, AAAI-91, Pittsburgh, PA, pp. 8-14.
14. Mahadevan, S. and Connell, J. 1991b. Scaling reinforcement learning to robotics by exploiting the subsumption architecture. In Eighth International Workshop on Machine Learning, Morgan Kaufmann, pp. 328-337.
15. Matarić, M.J. 1992a. Behavior-based systems: Key properties and implications. In IEEE International Conference on Robotics and Automation, Workshop on Architectures for Intelligent Control Systems, Nice, France, pp. 46-54.
16. Matarić, M.J. 1992b. Designing emergent behaviors: From local interactions to collective intelligence. In From Animals to Animats: International Conference on Simulation of Adaptive Behavior, J.-A. Meyer, H. Roitblat, and S. Wilson (Eds.).
17. Matarić, M.J. 1993. Kin recognition, similarity, and group behavior. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society, Boulder, Colorado, pp. 705-710.
18. Matarić, M.J. 1994a. Interaction and intelligent behavior, Technical Report AI-TR-1495, MIT Artificial Intelligence Lab.
19. Matarić, M.J. 1994b. Learning to behave socially. In From Animals to Animats: International Conference on Simulation of Adaptive Behavior, D. Cliff, P. Husbands, J.-A. Meyer, and S. Wilson (Eds.), pp. 453-462.
20. Millán, J.D.R. 1994. Learning reactive sequences from basic reflexes. In Proceedings, Simulation of Adaptive Behavior SAB-94, The MIT Press: Brighton, England, pp. 266-274.
21. Moore, A.W 1992. Fast, robust adaptive control by learning only forward models. Advances in Neural Information Processing, 4:571-579.
22. Parker, L.E. 1994. Heterogeneous multi-robot cooperation, Ph.D. thesis, MIT.
23. Pomerleau, D.A. 1992. Neural network perception for mobile robotic guidance, Ph.D. thesis, Carnegie Mellon University, School of Computer Science.
24. Schaal, S. and Atkeson, C.C. 1994. Robot juggling: An implementation of memory-bassed learning. Control Systems Magazine, 14:57-71.
25. Sutton, R. 1988. Learning to predict by method of temporal differences. Machine Learning, 3(1):9-44.
26. Tan, M. 1993. Multi-agent reinforcement learning: Independent vs. cooperative agents. In Proceedings, Tenth International Conference on Machine Learning, Amherst, MA, pp. 330-337.
27. Thrun, S.B. and Mitchell, T.M. 1993. Integrating inductive neural network learning and explanation-based learning. In Proceedings, IJCAI-93, Chambery, France.
28. Watkins, C.J.C.H. and Dayan, P. 1992. Q-learning. Machine Learning, 8:279-292.
29. Whitehead, S.D., Karlsson, J., and Tenenberg, J. 1993. Learning multiple goal behavior via task decomposition and dynamic policy merging. In Robot Learning, J.H. Connell and S. Mahadevan (Eds.), Kluwer Academic Publishers, pp. 45-78.