Intrinsically motivated neuroevolution for vision-based reinforcement learning

2011 IEEE International Conference on Development and Learning, ICDL 2011

Volumn , Issue , 2011, Pages

  Cuccu, Giuseppe ^a   Luciw, Matthew ^a   Schmidhuber, Jürgen ^a   Gomez, Faustino ^a  

^a UNIVERSITY OF LUGANO   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL EVOLUTION; CARDINALITIES; CONTINUOUS REINFORCEMENT; EMBEDDED AGENTS; HIGH-DIMENSIONAL; LARGE NETWORKS; LEARNING TASKS; NEURAL NETWORK CONTROLLERS; NEUROEVOLUTION; STATE VECTOR; VIDEO IMAGE; VISION BASED; VISUAL IMAGE;

COMPRESSORS; RECURRENT NEURAL NETWORKS; REINFORCEMENT LEARNING;

CONTROLLERS;

EID: 80055009389     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DEVLRN.2011.6037324     Document Type: Conference Paper

References (27)

1. F. Fernández and D. Borrajo. Two steps reinforcement learning. International Journal of Intelligent Systems, 23(2):213-245, 2008.
2. D. Floreano, P. Dürr, and C. Mattiussi. Neuroevolution: from architectures to learning. Evolutionary Intelligence, 1(1):46-62, 2008.
3. J. Gauci and K. Stanley. Generating large-scale neural networks through discovering geometric regularities. In Proceedings of the Conference on Genetic and Evolutionary Computation, pages 997-1004, New York, NY, USA, 2007. ACM.
4. L. Gisslen, M. Luciw, V. Graziano, and J. Schmidhuber. Sequential Constant Size Compressors and Reinforcement Learning. In Proceedings of the Fourth Conference on Artificial General Intelligence, 2011.
5. T. Glasmachers, T. Schaul, Y. Sun, D. Wierstra, and J. Schmidhuber. Exponential Natural Evolution Strategies. In Genetic and Evolutionary Computation Conference (GECCO), Portland, OR, 2010.
6. F. J. Gomez. Robust Nonlinear Control through Neuroevolution. PhD thesis, Department of Computer Sciences, University of Texas at Austin, 2003.
7. R. Gray. Vector quantization. IEEE Assp Magazine, 1(2):4-29, 1984.
8. F. Gruau. Neural Network Synthesis using Cellular Encoding and the Genetic Algorithm. PhD thesis, l'Universite Claude Bernard-Lyon 1, France, 1994.
9. N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary computation, 9(2):159-195, 2001.
10. T. Heskes. Energy functions for self-organizing maps. Kohonen maps, pages 303-316, 1999.
11. S. R. Jodogne and J. H. Piater. Closed-loop learning of visual control policies. Journal of Artificial Intelligence Research, 28:349-391, 2007.
12. J. Koutnik, F. Gomez, and J. Schmidhuber. Evolving neural networks in compressed weight space. In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO-10), 2010.
13. S. Lange and M. Riedmiller. Deep auto-encoder neural networks in reinforcement learning. In International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain. Citeseer, 2010.
14. R. Legenstein, N. Wilbert, and L. Wiskott. Reinforcement Learning on Slow Features of High-Dimensional Input Streams. PLoS Computational Biology, 6(8), 2010.
15. A. Moore. Knowledge of knowledge and intelligent experimentation for learning control. In Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on, volume 2, pages 683-688. IEEE, 1991.
16. D. Pierce and B. Kuipers. Map learning with uninterpreted sensors and effectors. Artificial Intelligence, 92:169-229, 1997.
17. T. Schaul, T. Glasmachers, and J. Schmidhuber. High dimensions and heavy tails for natural evolution strategies. In Genetic and Evolutionary Computation Conference (GECCO), 2011.
18. J. Schmidhuber. A possibility for implementing curiosity and boredom in model-building neural controllers. In J. A. Meyer and S. W. Wilson, editors, Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pages 222-227. MIT Press/Bradford Books, 1991.
19. J. Schmidhuber. Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science, 18(2):173-187, 2006.
20. J. Schmidhuber. Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990-2010). Autonomous Mental Development, IEEE Transactions on, 2(3):230-247, 2010.
21. S. Singh and R. Sutton. Reinforcement learning with replacing eligibility traces. Recent Advances in Reinforcement Learning, pages 123-158, 1996.
22. K. O. Stanley. Efficient Evolution of Neural Networks Through Complexification. PhD thesis, Department of Computer Sciences, University of Texas at Austin, August 2004. Technical Report AI-TR-04-314.
23. Y. Sun, D. Wierstra, T. Schaul, and J. Schmidhuber. Efficient Natural Evolution Strategies. In Genetic and Evolutionary Computation Conference (GECCO), 2009.
24. Y Sun, D. Wierstra, T. Schaul, and J. Schmidhuber. Stochastic Search using the Natural Gradient. In International Conference on Machine Learning (ICML), number 1, 2009.
25. R. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. Advances in neural information processing systems, pages 1038-1044, 1996.
26. D. Wierstra, T. Schaul, J. Peters, and J. Schmidhuber. Natural Evolution Strategies. In Proceedings of the Congress on Evolutionary Computation (CEC08), Hongkong. IEEE Press, 2008.
27. X. Yao. A review of evolutionary artificial neural networks. International Journal of Intelligent Systems, 4:203-222, 1993.