PyBrain

Journal of Machine Learning Research

Volumn 11, Issue , 2010, Pages 743-746

  Schaul, Tom ^a   Bayer, Justin ^a   Wierstra, Daan ^a   Sun, Yi ^a   Felder, Martin ^b   Sehnke, Frank ^b   Rückstieß, Thomas ^b   Schmidhuber, Jürgen ^b  

^a UNIVERSITY OF LUGANO   (Switzerland)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Neural networks; Optimization; Python; Reinforcement learning

Indexed keywords

BELIEF NETWORKS; MACHINE-LEARNING; POLICY GRADIENT METHODS; PYTHON; SHORT TERM MEMORY;

GRADIENT METHODS; HIGH LEVEL LANGUAGES; RECURRENT NEURAL NETWORKS; REINFORCEMENT; REINFORCEMENT LEARNING;

LEARNING ALGORITHMS;

EID: 77949523247     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (6)

1. Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural Computation, 9:1735-1780, 1997. ISSN 0899-7667.
2. Thomas Rückstieß, Martin Felder, and Jürgen Schmidhuber. State-dependent exploration for policy gradient methods. In Proceedings of the European Conference on Machine Learning (ECML), 2008.
3. Tom Schaul and Jürgen Schmidhuber. Scalable neural networks for board games. In International Conference on Artificial Neural Networks (ICANN), 2009.
4. Frank Sehnke, Christian Osendorfer, Thomas Rückstieß, Alex Graves, Jan Peters, and Jürgen Schmidhuber. Policy gradients with parameter-based exploration for control. In Proceedings of the International Conference on Artificial Neural Networks (ICANN), 2008.
5. Yi Sun, DaanWierstra, Tom Schaul, and Jürgen Schmidhuber. Efficient natural evolution strategies. In Genetic and Evolutionary Computation Conference (GECCO), 2009.
6. DaanWierstra, Tom Schaul, Jan Peters, and Jürgen Schmidhuber. Fitness expectation maximization. In Lecture Notes in Computer Science, Parallel Problem Solving from Nature - PPSN X, pages 337-346. Springer-Verlag, 2008.