Transfer in Reinforcement Learning Domains - ISBN:3642018815

Transfer in Reinforcement Learning Domains

Date: 30 April 2011, 05:19

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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 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

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