Reinforced Variational Inference Theophane Weber∗1 Nicolas Heess∗1 S. M. Ali Eslami1 John Schulman2 David Wingate3 David Silver1 1 Google DeepMind 2 University of California, Berkeley 3 Brigham Young University 1

Source URL: arkitus.com

Distributed Computing Prof. R. Wattenhofer Topics in Deep Reinforcement Learning In 2015 Google Deepmind published their DQN paper in which they present

Source URL: www.tik.ee.ethz.ch

Published as a conference paper at ICLRN EURAL P ROGRAMMER -I NTERPRETERS Scott Reed & Nando de Freitas Google DeepMind London, UK

Source URL: arxiv.org

Carl Doersch Research Scientist Google DeepMind London, UK web:

Source URL: www.carldoersch.com

Matching Networks for One Shot Learning arXiv:1606.04080v1 [cs.LG] 13 Jun 2016 Oriol Vinyals Google DeepMind

Source URL: arxiv.org

Latent Predictor Networks for Code Generation Wang Ling♦ Edward Grefenstette♦ Karl Moritz Hermann♦ Tom´asˇ Koˇcisk´y♦♣ Andrew Senior♦ Fumin Wang♦ Phil Blunsom♦♣ ♦Google DeepMind ♣University of O

Source URL: arxiv.org

One-shot Learning with Memory-Augmented Neural Networks Adam Santoro Google DeepMind ADAMSANTORO @ GOOGLE . COM

Source URL: arxiv.org

Recurrent Models of Visual Attention Volodymyr Mnih Nicolas Heess Alex Graves Google DeepMind

Source URL: papers.nips.cc

Generalization and Exploration via Randomized Value Functions Ian Osband1,2 Benjamin Van Roy1 Zheng Wen1,3 1 Stanford University, 2 Google Deepmind, 3 Adobe Research

Source URL: jmlr.org

Increasing the Action Gap: New Operators for Reinforcement Learning Marc G. Bellemare and Georg Ostrovski and Arthur Guez Philip S. Thomas∗ and R´emi Munos Google DeepMind {bellemare,ostrovski,aguez,munos}@google.com;

Source URL: psthomas.com

• Mathematics
• Mathematical optimization
• Dynamic programming
• Mathematical analysis
• Equations
• Operations research
• Systems theory
• Stochastic control
• Bellman equation
• Markov decision process
• Q-learning
• Reinforcement learning