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Reinforcement learning
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61	Minimax Regret Bounds for Reinforcement Learning Appendices We begin by introducing some notation in Sect. B and Sect. A. We then provide the full analysis of UCBVI in Sect. C. A. Table of Notation Add to Reading List Source URL: proceedings.mlr.press - Date: 2018-02-06 15:06:57
62	Mapping Instructions and Visual Observations to Actions with Reinforcement Learning Dipendra Misra† , John Langford‡ , and Yoav Artzi† † Dept. of Computer Science and Cornell Tech, Cornell University, New York, Add to Reading List Source URL: yoavartzi.com - Date: 2018-04-02 13:13:18
63	Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning Oron Anschel 1 Nir Baram 1 Nahum Shimkin 1 Abstract Instability and variability of Deep Reinforcement Add to Reading List Source URL: proceedings.mlr.press - Date: 2018-02-06 15:06:57
64	Reinforcement Learning for Mapping Instructions to Actions with Reward Learning Dipendra Misra Yoav Artzi Add to Reading List Source URL: yoavartzi.com - Date: 2018-04-02 13:13:18
65	Modular Multitask Reinforcement Learning with Policy Sketches A. Tasks and Sketches The complete list of tasks, sketches, and symbols is given below. Tasks marked with an asterisk⇤ are held out for the generalization Add to Reading List Source URL: proceedings.mlr.press - Date: 2018-02-06 15:06:57
66	Deep Reinforcement Learning with Double Q-learning Hado van Hasselt and Arthur Guez and David Silver arXiv:1509.06461v3 [cs.LG] 8 DecGoogle DeepMind Add to Reading List Source URL: arxiv.org - Date: 2015-12-09 21:35:51
67	Magical Policy Search Magical Policy Search: Data Efficient Reinforcement Learning with Guarantees of Global Optimality Philip S. Thomas Add to Reading List Source URL: ewrl.files.wordpress.com - Date: 2016-11-21 10:27:44
68	Reinforcement Learning to adjust Robot Movements to New Situations Jens Kober Erhan Oztop Add to Reading List Source URL: www.ias.informatik.tu-darmstadt.de - Date: 2012-02-21 11:46:33
69	Reinforcement Learning to Adjust Robot Movements to New Situations Jens Kober MPI Tübingen, Germany Erhan Oztop Add to Reading List Source URL: www.ias.informatik.tu-darmstadt.de - Date: 2012-02-21 11:46:27
70	Maximum Entropy Deep Inverse Reinforcement Learning ´ ska and Ingmar Posner Markus Wulfmeier and Peter Ondruˇ Mobile Robotics Group Department of Engineering Science University of Oxford Add to Reading List Source URL: www.robots.ox.ac.uk - Date: 2015-11-26 08:56:02

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