Open AccessBook
Reinforcement Learning: An Introduction
TLDR
This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.Abstract:
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.read more
Citations
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Proceedings Article
Speaker-Follower Models for Vision-and-Language Navigation
Daniel Fried,Ronghang Hu,Volkan Cirik,Anna Rohrbach,Jacob Andreas,Louis-Philippe Morency,Taylor Berg-Kirkpatrick,Kate Saenko,Dan Klein,Trevor Darrell +9 more
TL;DR: Experiments show that all three components of this approach---speaker-driven data augmentation, pragmatic reasoning and panoramic action space---dramatically improve the performance of a baseline instruction follower, more than doubling the success rate over the best existing approach on a standard benchmark.
Journal ArticleDOI
Learning of sequential movements by neural network model with dopamine-like reinforcement signal.
Roland E. Suri,Wolfram Schultz +1 more
TL;DR: This study explored a neural network model that used a reward-prediction error signal strongly resembling dopamine responses for learning movement sequences and efficiently allowed the model to learn long sequences.
Posted Content
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
TL;DR: This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resourcemanagement in the MAC layer, networking and mobility management in the network layer, and localization in the application layer.
Proceedings ArticleDOI
Regularization and feature selection in least-squares temporal difference learning
J. Zico Kolter,Andrew Y. Ng +1 more
TL;DR: This paper proposes a regularization framework for the LSTD algorithm, which is robust to irrelevant features and also serves as a method for feature selection, and presents an algorithm similar to the Least Angle Regression algorithm that can efficiently compute the optimal solution.
Posted Content
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing
Jie Xu,Lixing Chen,Shaolei Ren +2 more
TL;DR: This paper proposes an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost).
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
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