Solving non-Markovian control tasks with neuroevolution

IJCAI International Joint Conference on Artificial Intelligence

Volumn 2, Issue , 1999, Pages 1356-1361

  Gomez, Faustino J ^a   Miikkulainen, Risto ^a  

^a University of Texas at Austin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIRECT EVOLUTIONS; EVOLUTIONARY METHOD; INCREMENTAL METHOD; LEARNING EFFICIENCY; LOCAL SEARCH TECHNIQUES; NEURO EVOLUTIONS; SUB-POPULATIONS; VELOCITY INFORMATION;

ARTIFICIAL INTELLIGENCE;

POLES;

EID: 84880666558     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Charles W. Anderson. Learning to control an inverted pendulum using neural networks. IEEE Control Systems Magazine, 9:31-37, April 1989.
2. Marco Colombetti and Marco Dorigo. Robot shaping: Developing situated agents through learning. Technical Report TR-92-040, International Computer Science Institute, Berkeley, CA, 1992.
3. S. Dominic, R. Das. D. Whitley, and C. Anderson. Genetic reinforcement learning for neural networks. In Proceedings of the International Joint Conference on Neural Networks (Seattle, WA), volume II, pages 71-76, Piscataway, NJ, 1991. IEEE.
4. Jeffrey L. Elman. Incremental learning, or The importance of starting small. In Proceedings of the 13th Annual Conference of the Cognitive Science Society, pages 443-448, Hillsdale, NJ, 1991. Erlbaum.
5. Faustino Gomez and Risto Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317-342, 1997.
6. Frederic Gruau, Darrell Whitley, and Larry Pyeatt. A comparison between cellular encoding and direct encoding for genetic neural networks. Technical Report NC-TR-96-048, NeuroCOLT, 1996.
7. Donald Michie and R. A. Chambers. BOXES: An experiment in adaptive control. In E. Dale and D. Michie, editors, Machine Intelligence. Oliver and Boyd, Edinburgh, UK, 1968.
8. David E. Moriarty and Risto Miikkulainen. Efficient reinforcement learning through symbiotic evolution. Machine Learning, 22:11-32, 1996.
9. David E. Moriarty. Symbiotic Evolution of Neural Networks in Sequential Decision Tasks. PhD thesis, Department of Computer Sciences, The University of Texas at Austin, 1997. Technical Report UT-AI97-257.
10. Simon Perkins and Gillian Hayes. Robot shaping-principles, methods, and architectures. Technical Report 795, Department of Artifical Intelligence, University of Edinburgh, 1996.
11. Mark B. Ring. Continual Learning in Reinforcement Environments. PhD thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, Texas 78712, August 1994.
12. N. Saravanan and David B. Fogel. Evolving neural control systems. IEEE Expert, pages 23-27, June 1995.
13. J Schaffer and R Cannon. On the control of unstable mechanincal systems. In Automatic and Remote Control III: Proceedings of the Third Congress of the International Federation of Automatic Control, 1966.
14. S. Singh. Transfer of learning by composing solutions of elemental sequential tasks. Machine Learning, 8:323-339, 1992.
15. Richard Sutton. Temporal Credit Assignment in Reinforcement Learning. PhD thesis, University of Massachusetts, Amherst, MA, 1984.
16. D. Whitley, K. Mathias, and P. Fitzhorn. Delta-coding: An iterative search strategy for genetic algortihms. In Proceedings of the Fourth International Conference on Genetic Algorithms, Los Altos, CA, 1991. Morgan Kaufmann.
17. Alexis Wieland. Evolving neural network controllers for unstable systems. In Proceedings of the International Joint Conference on Neural Networks (Seattle, WA), volume II, pages 667-673, Piscataway, NJ, 1991. IEEE.