Similarities and differences between policy gradient methods and evolution strategies

ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning

Volumn , Issue , 2008, Pages 149-154

  Heidrich Meisner, Verena ^a   Igel, Christian ^a  

^a RUHR UNIVERSITY BOCHUM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COVARIANCE MATRIX ADAPTATION EVOLUTION STRATEGIES; EVOLUTION STRATEGIES; POLICY GRADIENT METHODS; VARIABLE METRIC METHODS;

BENCHMARKING; GRADIENT METHODS; NEURAL NETWORKS; REINFORCEMENT LEARNING;

EVOLUTIONARY ALGORITHMS;

EID: 84886993021     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. J. Peters, S. Vijayakumar, and S. Schaal. Reinforcement learning for humanoid robotics. In Proc. 3rd IEEE-RAS Int'l Conf. on Humanoid Robots, pages 29-30, 2003.
2. J. Peters and S. Schaal. Applying the episodic natural actor-critic architecture to motor primitive learning. In Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), pages 1-6. Evere, Belgien: d-side publications, 2007.
3. N. Hansen. The CMA evolution strategy: A comparing review. In Towards a new evolutionary computation. Advances on estimation of distribution algorithms., pages 75-102. Springer-Verlag, 2006.
4. D. Wierstra, T. Schaul, J. Peters, and J. Schmidhuber. Natural evolution strategies. Accepted at IEEE World Congress on Computational Intelligence (WCCI 2008).
5. V. Heidrich-Meisner, M. Lauer, C. Igel, and M. Riedmiller. Reinforcement learning in a Nutshell. In 15th European Symposium on Artificial Neural Networks (ESANN 2007), pages 277-288. Evere, Belgien: d-side publications, 2007.
6. R.S. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Advances in Neural Information Processing Systems, volume 12, pages 1057-1063, 2000.
7. J. Bagnell and J. Schneider. Covariant policy search. In Proc. 18th Int'l Joint Conf. on Artificial Intelligence, pages 1019-1024, 2003.
8. N. Hansen, S.D. Müller, and P. Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation, 11(1):1-18, 2003.
9. A. Pellecchia, C. Igel, J. Edelbrunner, and G. Schöner. Making driver modeling attractive. IEEE Intelligent Systems, 20(2):8-12, 2005.
10. M. Riedmiller, J. Peters, and S. Schaal. Evaluation of policy gradient methods and variants on the cart-pole benchmark. In Proc. IEEE Int'l Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL 2007), pages 254-261, 2007.