Reinforcement learning with artificial neural networks in python
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examples
model
.gitignore
LICENSE.txt
README.txt
__init__.py

README.txt

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. [1])
Q-learning long short-term memory network (cf. [2])
Long short-term memory network model/Q-learning feedforward neural network 
controller (cf. [3], Sec. 5.1)

Examples
--------
See the examples directory.

References
----------

[1] 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).

[2] Bakker, Pieter Bram. The State of Mind: Reinforcement Learning with 
Recurrent Neural Networks. PhD Thesis, Leiden University, 2004.

[3] 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).