Transfer in Reinforcement Learning Domains

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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 learnin...

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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 learnin...

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  • Formats: pdf
  • ISBN: 9783642018824
  • Publication Date: 19 May 2009
  • Publisher: Springer Berlin Heidelberg
  • Product language: English
  • Drm Setting: DRM