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• Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion
  Nate Kohl and Peter Stone Proceedings of the IEEE International Conference on Robotics and Automation, May 2004.
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• Evolving Keepaway Soccer Players through Task Decomposition
  Shimon Whiteson and Nate Kohl and Risto Miikkulainen and Peter Stone Machine Learning59(1):5--30May 2005.
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• Hierarchical Policy Gradient Algorithms
  M. Ghavamzadeh and Sridhar Mahadevan Proceedings of the Twentieth Conference on Machine Learning ICML-2003, (2003)
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• Reinforcement Learning for RoboCup-Soccer Keepaway
  Peter Stone and Richard S. Sutton and Gregory Kuhlmann Adaptive Behavior13(3):165--188(2005)
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• Reinforcement learning with long short-term memory
  B. Bakker Advances in Neural Information Processing Syst., 14, (2002)
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• Natural Actor-Critic
  J. Peters and S. Vijayakumar and S. Schaal Proceedings of the 16th European Conference on Machine Learning ECML 2005, page 280--291. (2005)
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• Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation
  Nicol Schraudolph and Jin Yu and Douglas Aberdeen Advances in Neural Information Processing Systems 18, MIT Press, Cambridge, MA, (2006)
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• Reinforcement Learning Through Gradient Descent
  L. Baird (1999)
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• A learning algorithm for continually running fully recurrent networks
  R. J. Williams and D. Zipser Neural Computation1(2):270--280(1989)
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• Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and LSTM
  J. Schmidhuber and F. Gers and D. Eck Neural Computation14(9):2039--2041(2002)
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• Gradient flow in recurrent nets: the difficulty of learning long-term dependencies
  S. Hochreiter and Y. Bengio and P. Frasconi and J. Schmidhuber A Field Guide to Dynamical Recurrent Neural Networks, IEEE Press, (2001)
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• RNN overview
  J. Schmidhuber (2004)
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• Reinforcement learning and its application to control
  V. Gullapalli (1992)
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• Two-phase nozzle and hollow core jet experiments
  J. Klockgether and H. P. Schwefel Proc. 11th Symp. Engineering Aspects of Magnetohydrodynamics, page 141--148. (1970)
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• Stochastic optimization and the simultaneous perturbation method
  James C. Spall WSC '99: Proceedings of the 31st conference on Winter simulation, page 101--109. New York, NY, USA, ACM, (1999)
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• Swarm intelligence
  James Kennedy and Russell C. Eberhart Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, (2001)
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• Optimization by Simulated Annealing
  S. Kirkpatrick and C. D. Gelatt and M. P. Vecchi Science, Number 4598, 13 May 1983(1983)
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• Natural Gradient Works Efficiently in Learning
  S. Amari Neural Computation10(2):251--276(1998)
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• Similarities and differences between policy gradient methods and evolution strategies
  Verena H. Meisner and Christian Igel To appear in: 16th European Symposium on Artificial Neural Networks ESANN, (2008)
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• A Tutorial on the Cross-Entropy Method
  P. De Boer and Kroese and S. Mannor and R. Y. Rubinstein Annals of Operations Research, 134, page 19--67. (2004)
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