Link

Authors

  • Huaqing Zhang - Google
  • Ruitong Huang - Borealis AI
  • Shanghang Zhang* - University of California, Berkeley (shzhang.pku[at]gmail.com)

Abstract

In this chapter, reinforcement learning is analyzed from the perspective of learning and planning. We initially introduce the concepts of model and model-based methods, with the highlight of advantages on model planning. In order to include the benefits of both model-based and model-free methods, we present the integration architecture combining learning and planning, with detailed illustration on Dyna-Q algorithm. Finally, for the integration of learning and planning, the simulation-based search applications are analyzed.

Keywords: model-based, model-free, Dyna, Monte-Carlo Tree Search, Temperal Difference (TD) search

Citation

To cite this book, please use this bibtex entry:

@incollection{deepRL-chapter9-2020,
 title={Integrating Learning and Planning},
 chapter={9},
 author={Huaqing Zhang, Ruitong Huang, Shanghang Zhang},
 editor={Hao Dong, Zihan Ding, Shanghang Zhang},
 booktitle={Deep Reinforcement Learning: Fundamentals, Research, and Applications},
 publisher={Springer Nature},
 pages={307-316},
 note={\url{http://www.deepreinforcementlearningbook.org}},
 year={2020}
}

If you find any typos or have suggestions for improving the book, do not hesitate to contact with the corresponding author (name with *).