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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
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Advances in Variational Inference
TL;DR: An overview of recent trends in variational inference is given and a summary of promising future research directions is provided.
Journal ArticleDOI
A Generalization Error for Q-Learning
TL;DR: This work considers Q-learning with function approximation for this setting and derives an upper bound on the generalization error in terms of quantities minimized by a Q- Learning algorithm, the complexity of the approximation space and an approximation term due to the mismatch between Q- learning and the goal of learning a policy that maximizes the value function.
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Action Dominates Valence in Anticipatory Representations in the Human Striatum and Dopaminergic Midbrain
Marc Guitart-Masip,Lluís Fuentemilla,Dominik R. Bach,Quentin J. M. Huys,Peter Dayan,Raymond J. Dolan,Emrah Düzel +6 more
TL;DR: During anticipation, in the striatum and a lateral region within the substantia nigra/ventral tegmental area (SN/VTA), action representations dominated over valence representations, challenging a conventional and dominant association between these areas and state value representations.
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Statistics of Midbrain Dopamine Neuron Spike Trains in the Awake Primate
TL;DR: Examining the relationship between the tonic and burst modes of activity in dopamine neurons while monkeys were performing a reinforced visuo-saccadic movement task revealed that bursts and pauses seemed to provide a continuous, although nonlinear, representation of the theoretically defined reward prediction error of reinforcement learning.
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Chasing a Moving Target: Exploitation and Exploration in Dynamic Environments
TL;DR: Sorensen et al. as discussed by the authors developed a computational model based on the canonical multi-armed bandit formulation of exploration and exploitation to understand the mechanisms by which environmental change acts to make purposeful efforts at organizational adaptation less (or more) valuable.
References
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Journal ArticleDOI
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.
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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.
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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.
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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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