Continuous-time on-policy neural reinforcement learning of working memory tasks Davide Zambrano Pieter R. Roelfsema

Source URL: rll.berkeley.edu

arXiv:1511.06581v3 [cs.LG] 5 AprDueling Network Architectures for Deep Reinforcement Learning Ziyu Wang Tom Schaul

Source URL: arxiv.org

Offline Evaluation of Online Reinforcement Learning Algorithms Travis Mandel1 , Yun-En Liu2 , Emma Brunskill3 , and Zoran Popovi´c1,2 1 Center for Game Science, Computer Science & Engineering, University of Washington,

Source URL: grail.cs.washington.edu

The Journal of Neuroscience, May 27, 2015 • 35(21):8145– 8157 • 8145 Behavioral/Cognitive Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms

Source URL: gershmanlab.webfactional.com

MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Deep Reinforcement Learning for Partial Differential Equation Control Farahmand, A.-M.; Nabi, S.; Nikovski, D.N.

Source URL: www.merl.com

arXiv:1602.01783v2 [cs.LG] 16 JunAsynchronous Methods for Deep Reinforcement Learning Volodymyr Mnih1 Adrià Puigdomènech Badia1

Source URL: arxiv.org

• Machine learning algorithms
• Machine learning
• Artificial intelligence
• Learning
• Computational neuroscience
• Artificial neural networks
• Computational statistics
• Reinforcement learning
• Q-learning
• Convolutional neural network
• Temporal difference learning
• Demis Hassabis

Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control Yangchen Pan 1 2 Amir-massoud Farahmand 3 2 Martha White 1 Saleh Nabi 2 Piyush Grover 2 Daniel Nikovski 2

Source URL: sologen.net

Deep Reinforcement Learning for General Video Game AI Ruben Rodriguez Torrado* Philip Bontrager* New York University New York, NY

Source URL: www.diego-perez.net

Articles DOI: s41562The successor representation in human reinforcement learning I. Momennejad   1*, E. M. Russek2, J. H. Cheong3, M. M. Botvinick   4, N. D. Daw1 and S. J. Gershman   5

Source URL: gershmanlab.webfactional.com