Open Access

Combining Local and Global Direct Derivative-Free Optimization for Reinforcement Learning

1. Barash, D.. A genetic search in policy space for solving Markov decision processes, - In: AAAI Spring Symposium on Search Techniques for Problem Solving under Uncertainty and Incomplete Information, 1999.Search in Google Scholar

2. Baxter, J., P. Bartlett. Direct gradient-based reinforcement learning, - In: Proceedings of IEEE International Symposium on Circuits and Systems, Vol. 3, IEEE, 2000, 271-274.Search in Google Scholar

3. Brachetti, P., M. De Felice Ciccoli, G. Di Pillo, S. Lucidi. A new version of the Price’s algorithm for global optimization, Journal of Global Optimization, Vol. 10, No 2, 1997, 165-184.10.1023/A:1008250020656Search in Google Scholar

4. Carpin, S., M. Lewis, J. Wang, S. Balakirsky, C. Scrapper. Bridging the gap between simulation and reality in urban search and rescue, Robocup 2006: Robot Soccer World Cup X, 1-12.10.1007/978-3-540-74024-7_1Search in Google Scholar

5. Conn, A., K. Scheinberg, L. Vicente. Introduction to derivative-free optimization, Vol. 8, Society for Industrial Mathematics, 2009.10.1137/1.9780898718768Search in Google Scholar

6. Dorigo, M., M. Birattari, T. Stutzle. Ant colony optimization, IEEE Computational Intelligence Magazine, Vol. 1, No 4, 2006, 28-39.10.1109/CI-M.2006.248054Search in Google Scholar

7. Glover, F., M. Laguna. Tabu search, Vol. 1, Springer, 1998.10.1007/978-1-4615-6089-0_1Search in Google Scholar

8. Goldberg, D.. Genetic algorithms in search, optimization, and machine learning, Addisonwesley, 1989.Search in Google Scholar

9. Gomez, F., J. Schmidhuber, R. Miikkulainen. Efficient Non-Linear Control through Neuroevolution, - In: Proceedings of the European Conference on Machine Learning, Springer, Berlin, 2006, 654-662.10.1007/11871842_64Search in Google Scholar

10. Horst, R., P. Pardalos, N. Thoai. Introduction to global optimization, Springer, 2000.10.1007/978-1-4615-0015-5Search in Google Scholar

11. Jakobi, N., P. Husbands, I. Harvey. Noise and the reality gap: The use of simulation in evolutionary robotics, Advances in artificial life, 704-720.10.1007/3-540-59496-5_337Search in Google Scholar

12. Kakade, S.. A natural policy gradient, Advances in neural information processing systems, Vol. 14, 2001, 1531-1538.Search in Google Scholar

13. Kirkpatrick, S., C. Gelatt Jr, M. Vecchi. Optimization by simulated annealing, Science, Vol. 220, No 4598, 1983, 671-680.10.1126/science.220.4598.67117813860Search in Google Scholar

14. Kober, J., J. Peters. Policy search for motor primitives in robotics, Machine learning, Vol. 84, No 1, 2011, 171-203.10.1007/s10994-010-5223-6Search in Google Scholar

15. Kormushev, P., D. G. Caldwell. Simultaneous Discovery of Multiple Alternative Optimal Policies by Reinforcement Learning, - In: IEEE International Conference on Intelligent Systems (IS 2012), 2012.10.1109/IS.2012.6335136Search in Google Scholar

16. Lucidi, S., M. Sciandrone. On the global convergence of derivative-free methods for unconstrained optimization, SIAM Journal of Optimization, Vol. 13, No 1, 2002, 97-116.10.1137/S1052623497330392Search in Google Scholar

17. Peters, J., S. Schaal. Policy gradient methods for robotics, - In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2006, 2219-2225.10.1109/IROS.2006.282564Search in Google Scholar

18. Peters, J., S. Schaal. Reinforcement learning by reward-weighted regression for operational space control, - In: Proceedings of the 24th international conference on Machine learning, ACM, 2007, 745-750.10.1145/1273496.1273590Search in Google Scholar

19. Peters, J., S. Vijayakumar, S. Schaal. Natural Actor-Critic, - In: Proceedings of the 16th European Conference on Machine Learning (ECML), 2005, 280-291.10.1007/11564096_29Search in Google Scholar

20. Price, W.. Global optimization by controlled random search, Journal of Optimization Theory and Applications, Vol. 40, No 3, 1983, 333-348.10.1007/BF00933504Search in Google Scholar

21. Ribas, D., N. Palomeras, P. Ridao, M. Carreras, A. Mallios. Girona 500 AUV, from survey to intervention, IEEE/ASME Transactions on Mechatronics, Vol. 17, No 1, 2012, 46-53.10.1109/TMECH.2011.2174065Search in Google Scholar

22. Sutton, R., D. McAllester, S. Singh, Y. Mansour. Policy gradient methods for reinforcement learning with function approximation, Advances in neural information processing systems, Vol. 12, No 22.Search in Google Scholar

23. Theodorou, E., J. Buchli, S. Schaal. A generalized path integral control approach to reinforcement learning, The Journal of Machine Learning Research, Vol. 9999, 2010, 3137-3181.Search in Google Scholar

24. Torczon, V., et al .. On the convergence of pattern search algorithms, SIAM Journal on optimization, Vol. 7, No 1, 1997, 1-25.10.1137/S1052623493250780Search in Google Scholar

25. Torn, A., A. Zilinska s. Global Optimization, Springer, 1989.Search in Google Scholar

26. Williams, R.. Simple statistical gradient-following algorithms for connectionist reinforcement learning, Machine learning, Vol. 8, No 3, 1992, 229-256.10.1007/BF00992696Search in Google Scholar

eISSN:
1314-4081
ISSN:
1311-9702
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology