MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Truncated Approximate Dynamic Programming with Task-Dependent Terminal Value Farahmand, A.-M.; Nikovski, D.N.; Igarashi, Y.; Konaka, H.

Source URL: www.merl.com

• Mathematical optimization
• Operations research
• Dynamic programming
• Markov processes
• Stochastic control
• Markov decision process
• Reinforcement learning
• Convolution
• Optimal control
• Q-learning

Cognitive Science–618 Copyright © 2014 Cognitive Science Society, Inc. All rights reserved. ISSN: printonline DOI: cogsInferring Learners’ Knowledge From Their Ac

Source URL: cocosci.berkeley.edu

• Dynamic programming
• Markov processes
• Stochastic control
• Cognitive science
• Probability theory
• Markov decision process
• Reinforcement learning
• Theory of mind
• Probability
• Psychology
• Cognition

Playing Atari with Deep Reinforcement Learning Volodymyr Mnih Koray Kavukcuoglu

Source URL: koray.kavukcuoglu.org

Journal of Machine Learning Research–1105 Submitted 2/05; Published 6/06 Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems∗

Source URL: www.jmlr.org

Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning Jan Koutník Giuseppe Cuccu

Source URL: people.idsia.ch

• Artificial neural networks
• Cybernetics
• Applied mathematics
• Machine learning
• Recurrent neural network
• Backpropagation
• Jrgen Schmidhuber
• Deep learning
• Types of artificial neural networks

Targeting Specific Distributions of Trajectories in MDPs∗ David L. Roberts1 , Mark J. Nelson1 , Charles L. Isbell, Jr.1 , Michael Mateas1 , Michael L. Littman2 1 2

Source URL: www.kmjn.org

• Dynamic programming
• Markov processes
• Stochastic control
• Belief revision
• Reinforcement learning
• Markov decision process
• Probability distribution

Journal of Machine Learning Research1578 Submitted 11/13; Revised 11/14; Published 8/15 RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Research

Source URL: jmlr.org

Training Factor Graphs with Reinforcement Learning For Efficient MAP Inference Michael Wick, Khashayar Rohanimanesh, Sameer Singh, and Andrew McCallum University of Massachusetts Computer Science Department (IESL) Proba

Source URL: people.cs.umass.edu

Journal of Articial Intelligence Research Submitted 9/95; published 5/96 Reinforcement Learning: A Survey Leslie Pack Kaelbling

Source URL: www.jair.org

Playing Tetris with Deep Reinforcement Learning Matt Stevens Sabeek Pradhan

Source URL: cs231n.stanford.edu