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Control Optimization with Reinforcement Learning

  • Abhijit Gosavi
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 25)

Abstract

This chapter focuses on a relatively new methodology called reinforcement learning. A prerequisite for this chapter is the previous chapter. Reinforcement learning (RL) is essentially a form of simulation-based dynamic programming and is primarily used to solve Markov and semi-Markov decision problems. It is natural to wonder why the word “learning” is a part of the name then. The answer is: pioneering work in this area was done by the artificial intelligence community, which views it as a machine “learning” method.

Keywords

Reinforcement Learning Bellman Equation Policy Iteration Average Reward Reinforcement Learning Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Abhijit Gosavi
    • 1
  1. 1.Department of Industrial EngineeringThe State University of New YorkBuffaloUSA

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