Open AccessPosted Content
Reinforcement Learning: A Survey
TLDR
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.Abstract:
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, 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. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.read more
Citations
More filters
Journal ArticleDOI
Exploration of Multi-State Environments: Local Measures and Back-Propagation of Uncertainty
Nicolas Meuleau,Paul Bourgine +1 more
TL;DR: This paper presents an action selection technique for reinforcement learning in stationary Markovian environments that consists of assimilating the local measures of uncertainty to rewards, and back-propagating them with the dynamic programming or temporal difference mechanisms.
Journal ArticleDOI
Artificial intelligence in recommender systems
Qian Zhang,Jie Lu,Yaochu Jin +2 more
TL;DR: The paper carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning.
Journal ArticleDOI
Learning in quantum control: High-dimensional global optimization for noisy quantum dynamics
Pantita Palittapongarnpim,Peter Wittek,Peter Wittek,Ehsan Zahedinejad,Shakib Vedaie,Barry C. Sanders +5 more
TL;DR: This work employs differential evolution algorithms to circumvent the stagnation problem of non-convex optimization and improves quantum control fidelity for noisy system by averaging over the objective function and introduces heuristics for early termination of runs and for adaptive selection of search subspaces.
Journal ArticleDOI
A review of hybrid renewable energy systems in mini-grids for off-grid electrification in developing countries
Emília Inês Come Zebra,Emília Inês Come Zebra,Henny van der Windt,Geraldo Nhumaio,André Faaij +4 more
TL;DR: In this paper, the levelized cost of energy (LCOE) of different mini-grids was compared and analyzed, and the results reveal that diesel is the most expensive technology.
Journal ArticleDOI
Integrating Guidance into Relational Reinforcement Learning
Kurt Driessens,Sašo Džeroski +1 more
TL;DR: This paper presents a solution based on the use of “reasonable policies” to provide guidance in Relational reinforcement learning, which makes Q-learning feasible in structural domains by incorporating a relational learner into Q- learning.
References
More filters
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
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
Book
Dynamic Programming and Optimal Control
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
Book
Parallel and Distributed Computation: Numerical Methods
TL;DR: This work discusses parallel and distributed architectures, complexity measures, and communication and synchronization issues, and it presents both Jacobi and Gauss-Seidel iterations, which serve as algorithms of reference for many of the computational approaches addressed later.