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5 papers · 1 filter
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver +4
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 convolutio…
Unit Tests for Stochastic Optimization
Tom Schaul, Ioannis Antonoglou, David Silver
Optimization by stochastic gradient descent is an important component of many large-scale machine learning algorithms. A wide variety of such optimization algorithms have been devi…
Deep AutoRegressive Networks
Karol Gregor, Ivo Danihelka, Andriy Mnih +2
We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data. Successive deep stochastic hidden layers are equipped with aut…
Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling
L. A. Prashanth, Nathaniel Korda, Rémi Munos
We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm. Our propos…
Minimum length path decompositions
Dariusz Dereniowski, Wieslaw Kubiak, Yori Zwols
We consider a bi-criteria generalization of the pathwidth problem, where, for given integers and a graph , we ask whether there exists a path decomposition $\cP$ of su…