Transfer in Reinforcement Learning Domains

Matthew E. Taylor


Transfer in Reinforcement Learning Domains

Transfer in Reinforcement Learning Domains

  • Title: Transfer in Reinforcement Learning Domains
  • Author: Matthew E. Taylor
  • ISBN: 9783642018817
  • Page: 462
  • Format: Hardcover

Training Reinforcement Lever Transfer of Learning Do you find that once back in the workplace after your training programs, individuals fail to apply their learning effectively Training reinforcement is the solution. Successor Features for Transfer in Reinforcement Learning Transfer in reinforcement learning can be de ned in many different ways, but in general it refers to the notion that generalization should occur not only within a Transfer Deep Reinforcement Learning in D Environments Transfer Deep Reinforcement Learning in D Environments An Empirical Study Devendra Singh Chaplot School of Computer Science Carnegie Mellon University Knowledge Transfer for Deep Reinforcement Learning with Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay Haiyan Yin and Sinno Jialin Pan School of Computer Science and Engineering Transfer in Reinforcement Learning A Framework and a Transfer in reinforcement learning is a novel research area that focuses on the development of methods to transfer knowledge from a set of source tasks to a target task. Transfer Learning Machine Learning s Next Frontier This blog post gives an overview of transfer learning, The ability to transfer knowledge to new conditions is as unsupervised learning and reinforcement Transfer in Reinforcement Learning a Framework and a Survey Transfer in Reinforcement Learning a Framework and a Survey Alessandro Lazaric Abstract Transfer in reinforcement learning is a novel research area that focuses Successor Features for Transfer in Reinforcement Learning Successor Features for Transfer in Reinforcement Learning Andr Barreto, Will Dabney, Rmi Munos, Jonathan J Hunt, Tom Schaul, Hado van Hasselt, David Silver Improving Learning Transfer Training Magazine Learning transfer can be defined as the ability of a learner to Improving Learning Transfer and post training reinforcement to enhance the transfer of Transfer Reinforcement Learning with Shared Dynamics Transfer Reinforcement Learning with Shared Dynamics Romain Laroche Microsoft Maluuba rue Peel, Montreal QC, Canada romainroche microsoft



In reinforcement learning RL problems, learning agents sequentially execute actions with the goal of maximizing a reward signal The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge The key insightIn reinforcement learning RL problems, learning agents sequentially execute actions with the goal of maximizing a reward signal The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge The key insight behind transfer learning is that generalization may occur not only within tasks, but also across tasks While transfer has been studied in the psychological literature for many years, the RL community has only recently begun to investigate the benefits of transferring knowledge This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research The key contributions of this book are Definition of the transfer problem in RL domains Background on RL, sufficient to allow a wide audience to understand discussed transfer concepts Taxonomy for transfer methods in RL Survey of existing approaches In depth presentation of selected transfer methods Discussion of key open questions By way of the research presented in this book, the author has established himself as the pre eminent worldwide expert on transfer learning in sequential decision making tasks A particular strength of the research is its very thorough and methodical empirical evaluation, which Matthew presents, motivates, and analyzes clearly in prose throughout the book Whether this is your initial introduction to the concept of transfer learning, or whether you are a practitioner in the field looking for nuanced details, I trust that you will find this book to be an enjoyable and enlightening read Peter Stone, Associate Professor of Computer Science


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