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Reinforcement learning with artificial neural networks in python
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About ----- Reinforcement learning with artificial neural networks in python. This code is intended mainly as proof of concept of action-value learning by artificial neural networks, and was inspired by [1, 2, 3]. The implementations are not particularly clear, efficient, well tested or numerically stable. We advise against using this software for nondidactic purposes. This software is licensed under the MIT License. Algorithms ---------- Q-learning feedforward neural network (cf. ) Q-learning long short-term memory network (cf. ) Long short-term memory network model/Q-learning feedforward neural network controller (cf. , Sec. 5.1) Examples -------- See the examples directory. References ----------  Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013).  Bakker, Pieter Bram. The State of Mind: Reinforcement Learning with Recurrent Neural Networks. PhD Thesis, Leiden University, 2004.  Schmidhuber, J. On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models. arXiv preprint arXiv:1511.09249 (2015).