Q-learning in continuous state and action spaces

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

Volumn 1747, Issue , 1999, Pages 417-428

  Gaskett, Chris ^a   Wettergreen, David ^a   Zelinsky, Alexander ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS;

ACTION SPACES; CONTINUOUS ACTIONS; CONTINUOUS STATE; CONTROL POLICY; CONTROL TASK; DISCRETE STATE; ENHANCE LEARNING; NON-HOLONOMIC CONTROL;

ARTIFICIAL INTELLIGENCE;

EID: 84957629024     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-46695-9_35     Document Type: Conference Paper

References (17)

1. [1] J. S. Albus. A new approach to manipulator control: the cerebrellar model articulated controller (CMAC). J. Dynamic Systems, Measurement and Control, 97:220-227, 1975.
2. [2] Leemon C. Baird and A. Harry Klopf. Reinforcement learning with high-dimensional, continuous actions. Technical Report WL-TR-93-1147, Wright Laboratory, 1993.
3. [3] W. Baker and J. Farrel. An introduction to connectionist learning control systems. In D. A. White and D. A. Sofge, editors, Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches. Van Nostrand Reinhold, 1992.
4. [4] A. G. Barto, R. S. Sutton, and C. W. Anderson. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans on systems, man and cybernetics, SMC, 13:834-846, 1983.
5. [5] Chris Gaskett, David Wettergreen, and Alexander Zelinsky. Reinforcement learning applied to the control of an autonomous underwater vehicle. In Proceedings of the Australian Conference on Robotics and Automation (AuCRA99), 1999.
6. [6] H.-M. Gross, V. Stephan, and M. Krabbes. A neural field approach to topological reinforcement learning in continuous action spaces. In Proc. 1998 IEEE World Congress on Computational Intelligence, WCCI'98 and International Joint Conference on Neural Networks, IJCNN'98, Anchorage, Alaska, 1998.
7. [7] Mance E. Harmon and Leemon C. Baird. Residual advantage learning applied to a differential game. In Proceedings of the International Conference on Neural Networks, Washington D.C, 1995.
8. [8] T. Kohonen. Self-Organization and Associative Memory. Springer, Berlin, third edition, 1989.
9. [9] Peter Lancaster and Kestutis Salkauskas. Curve and Surface Fitting, an Introduction. Academic Press, 1986.
10. [10] Long-Ji Lin. Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning Journal, 8(3/4), 1992.
11. [11] Gavin Adrian Rummery. Problem solving with reinforcement learning. PhD thesis, Cambridge University, 1995.
12. [12] Juan C. Santamaria, Richard S. Sutton, and Ashwin Ram. Experiments with reinforcement learning in problems with continuous state and action spaces. Adaptive Behaviour, 6(2):163-218, 1998.
13. [13] Juan Miguel Santos. Contribution to the study and design of reinforcement functions. PhD thesis, Universidad de Buenos Aires, Universite d'Aix-Marseille 3, 1999.
14. [14] Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. Bradford Books, MIT, 1998.
15. [15] Claude F. Touzet. Neural reinforcement learning for behaviour synthesis. Robotics and Autonomous Systems, 22(3-4):251-81, 1997.
16. [16] Christopher J. C. H. Watkins. Learning from Delayed Rewards. PhD thesis, University of Cambridge, 1989.
17. [17] Paul J. Werbos. Approximate dynamic programming for real-time control and neural modeling. In D. A. White and D. A. Sofge, editors, Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches. Van Nostrand Reinhold, 1992.