Recent Advances in Reinforcement Learning
Editat de Leslie Pack Kaelblingen Limba Engleză Paperback – 7 dec 2010
Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area.
Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
Toate formatele și edițiile | Preț | Express |
---|---|---|
Paperback (1) | 632.40 lei 6-8 săpt. | |
Springer Us – 7 dec 2010 | 632.40 lei 6-8 săpt. | |
Hardback (1) | 638.56 lei 6-8 săpt. | |
Springer Us – 31 mar 1996 | 638.56 lei 6-8 săpt. |
Preț: 632.40 lei
Preț vechi: 790.50 lei
-20% Nou
Puncte Express: 949
Preț estimativ în valută:
121.07€ • 125.84$ • 100.38£
121.07€ • 125.84$ • 100.38£
Carte tipărită la comandă
Livrare economică 05-19 februarie 25
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781441951601
ISBN-10: 1441951601
Pagini: 296
Ilustrații: IV, 292 p.
Dimensiuni: 170 x 244 x 16 mm
Greutate: 0.42 kg
Ediția:Softcover reprint of the original 1st ed. 1996
Editura: Springer Us
Colecția Springer
Locul publicării:New York, NY, United States
ISBN-10: 1441951601
Pagini: 296
Ilustrații: IV, 292 p.
Dimensiuni: 170 x 244 x 16 mm
Greutate: 0.42 kg
Ediția:Softcover reprint of the original 1st ed. 1996
Editura: Springer Us
Colecția Springer
Locul publicării:New York, NY, United States
Public țintă
ResearchCuprins
Editorial.- Efficient Reinforcement Learning through Symbiotic Evolution.- Linear Least-Squares Algorithms for Temporal Difference Learning.- Feature-Based Methods for Large Scale Dynamic Programming.- On the Worst-Case Analysis of Temporal-Difference Learning Algorithms.- Reinforcement Learning with Replacing Eligibility Traces.- Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results.- The Loss from Imperfect Value Functions in Expectation-Based and Minimax-Based Tasks.- The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms.- Creating Advice-Taking Reinforcement Learners.- Technical Note.