Martin Riedmiller, Daan Wierstra, Ioannis Antonoglou, Alex Graves, David Silver, Koray Kavukcuoglu, Volodymyr Mnih - 2013
Publications: arXiv Add/Edit
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
Simple reinforcement learning project for a neural network playing connect four
Implementation of Google's paper on playing atari games using deep learning in python.
An implementation of a quantum error correction algorithm for bit-flip errors on the topological toric code using deep reinforcement learning.
License Plate Recognition Using Deep Learning