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On-line Q-learning Using Connectionist Systems
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This article is published in CTIT technical reports series.The article was published on 1994-01-01 and is currently open access. It has received 1128 citations till now. The article focuses on the topics: Line (text file).read more
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Journal ArticleDOI
Reinforcement learning: a survey
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Proceedings Article
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih,Adrià Puigdomènech Badia,Mehdi Mirza,Alex Graves,Tim Harley,Timothy P. Lillicrap,David Silver,Koray Kavukcuoglu +7 more
TL;DR: A conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers and shows that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.
Posted Content
Reinforcement Learning: A Survey
TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Journal ArticleDOI
Deep Reinforcement Learning: A Brief Survey
TL;DR: Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world as discussed by the authors.
Journal ArticleDOI
Transfer Learning for Reinforcement Learning Domains: A Survey
Matthew D. Taylor,Peter Stone +1 more
TL;DR: This article presents a framework that classifies transfer learning methods in terms of their capabilities and goals, and then uses it to survey the existing literature, as well as to suggest future directions for transfer learning work.