A Bandit Framework for Optimal Selection of Reinforcement Learning Agents

10 February 2019

Papers citing "A Bandit Framework for Optimal Selection of Reinforcement Learning Agents"

7 / 7 papers shown

Title
Bridging the Gap Between Value and Policy Based Reinforcement Learning Ofir Nachum Mohammad Norouzi Kelvin Xu Dale Schuurmans 176 478 0 28 Feb 2017
Combining policy gradient and Q-learning Brendan O'Donoghue Rémi Munos Koray Kavukcuoglu Volodymyr Mnih OffRL OnRL 100 140 0 05 Nov 2016
VIME: Variational Information Maximizing Exploration Rein Houthooft Xi Chen Yan Duan John Schulman F. Turck Pieter Abbeel 86 78 0 31 May 2016
Asynchronous Methods for Deep Reinforcement Learning Volodymyr Mnih Adria Puigdomenech Badia M. Berk Mirza Alex Graves Timothy Lillicrap Tim Harley David Silver Koray Kavukcuoglu 210 8,882 0 04 Feb 2016
Massively Parallel Methods for Deep Reinforcement Learning Arun Nair Praveen Srinivasan Sam Blackwell Cagdas Alcicek Rory Fearon ... Stig Petersen Shane Legg Volodymyr Mnih Koray Kavukcuoglu David Silver OffRL AI4CE GNN 114 505 0 15 Jul 2015
Trust Region Policy Optimization John Schulman Sergey Levine Philipp Moritz Michael I. Jordan Pieter Abbeel 285 6,807 0 19 Feb 2015
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments Yi Sun Faustino J. Gomez Jürgen Schmidhuber 117 163 0 29 Mar 2011