Atjaunināt sīkdatņu piekrišanu

Recent Advances in Reinforcement Learning: 9th European Workshop, EWRL 2011, Athens, Greece, September 9-11, 2011, Revised and Selected Papers [Mīkstie vāki]

3.00/5 (2 ratings by Goodreads)
Edited by , Edited by
  • Formāts: Paperback / softback, 345 pages, weight: 546 g, 98 Illustrations, black and white; XIII, 345 p. 98 illus., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 7188
  • Izdošanas datums: 22-May-2012
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642299458
  • ISBN-13: 9783642299452
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 46,91 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Standarta cena: 55,19 €
  • Ietaupiet 15%
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
Recent Advances in Reinforcement Learning: 9th European Workshop, EWRL 2011, Athens, Greece, September 9-11, 2011, Revised and Selected PapersPalielināt
  • Formāts: Paperback / softback, 345 pages, weight: 546 g, 98 Illustrations, black and white; XIII, 345 p. 98 illus., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 7188
  • Izdošanas datums: 22-May-2012
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642299458
  • ISBN-13: 9783642299452
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783642299452.html
Keywords:
This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse reinforcement learning and real-world reinforcement learning.
Invited Talk Abstracts.-Invited Talk: UCRL and Autonomous
Exploration.-Invited Talk: Increasing Representational Power and Scaling
Inference in Reinforcement Learning.-Invited Talk: PRISM Practical RL:
Representation, Interaction, Synthesis, and Mortality.-Invited Talk: Towards
Robust Reinforcement Learning Algorithms.-Online Reinforcement Learning
Automatic Discovery of Ranking Formulas for Playing with Multi-armed
Bandits.-Goal-Directed Online Learning of Predictive Models.-Gradient Based
Algorithms with Loss Functions and Kernels for Improved On-Policy
Control.-Learning and Exploring MDPs -Active Learning of MDP Models.-Handling
Ambiguous Effects in Action Learning.-Feature Reinforcement Learning in
Practice.-Function Approximation Methods for Reinforcement Learning
Reinforcement Learning with a Bilinear Q Function.-1-Penalized Projected
Bellman Residual.-Regularized Least Squares Temporal Difference Learning with
Nested 2 and 1 Penalization.-Recursive Least-Squares Learning with
Eligibility Traces.-Value Function Approximation through Sparse Bayesian
Modeling.-Automatic Construction of Temporally Extended Actions for MDPs
Using Bisimulation Metrics.-Unified Inter and Intra Options Learning Using
Policy Gradient Methods.-Options with Exceptions.-Policy Search and
Bounds.-Robust Bayesian Reinforcement Learning through Tight Lower
Bounds.-Optimized Look-ahead Tree Search Policies.-A Framework for Computing
Bounds for the Return of a Policy.-Multi-Task and Transfer Reinforcement
Learning.-Transferring Evolved Reservoir Features in Reinforcement Learning
Task.-Transfer Learning via Multiple Inter-task Mappings.-Multi-Task
Reinforcement Learning: Shaping and Feature Selection.-Multi-Agent
Reinforcement Learning.-Transfer Learning in Multi-Agent Reinforcement
Learning Domains.-An Extension of a Hierarchical Reinforcement Learning
Algorithm for Multiagent Settings.-Apprenticeship and Inverse Reinforcement
Learning Bayesian Multitask Inverse ReinforcementLearning.-Batch, Off-Policy
and Model-Free Apprenticeship Learning.-Real-World Reinforcement Learning
Introduction of Fixed Mode States into Online Profit Sharing and Its
Application to Waist Trajectory Generation of Biped Robot.-MapReduce for
Parallel Reinforcement Learning.-Compound Reinforcement Learning: Theory and
an Application to Finance.-Proposal and Evaluation of the Active Course
Classification Support System with Exploitation-Oriented Learning.