LETTER doi:nature14236 Human-level control through deep reinforcement learning

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• Artificial intelligence
• Computational neuroscience
• Machine learning
• Learning
• Machine learning algorithms
• Artificial neural networks
• Cybernetics
• Q-learning
• Reinforcement learning
• Convolutional neural network
• DQN
• DeepMind

Reinforcement learning and causal models Samuel J. Gershman Department of Psychology and Center for Brain Science Harvard University December 6, 2015

Source URL: gershmanlab.webfactional.com

OFFER: Off-Environment Reinforcement Learning Kamil Ciosek Shimon Whiteson Department of Computer Science, University of Oxford

Source URL: www.ciosek.net

GridFormation: Towards Self-Driven Online Data Partitioning using Reinforcement Learning Gabriel Campero Durand Marcus Pinnecke

Source URL: wwwiti.cs.uni-magdeburg.de

LETTER Communicated by Andrew Barto Robust Reinforcement Learning Jun Morimoto

Source URL: www.cns.atr.jp

Deep Reinforcement Learning in Large Discrete Action Spaces Gabriel Dulac-Arnold*, Richard Evans*, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Cop

Source URL: arxiv.org

Intensional data management Reinforcement learning Applications

Source URL: pierre.senellart.com

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

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Risk-Constrained Reinforcement Learning with Percentile Risk Criteria Yinlam Chow YCHOW @ STANFORD . EDU

Source URL: lucasjanson.rc.fas.harvard.edu

Constrained Policy Optimization Joshua Achiam 1 David Held 1 Aviv Tamar 1 Pieter Abbeel 1 2 Abstract For many applications of reinforcement learning it can be more convenient to specify both a reward function and constra

Source URL: proceedings.mlr.press