Corpus ID: 147701855

Reinforcement Learning and Optimal Control by Dimitri

@inproceedings{Bertsekas2019ReinforcementLA,
  title={Reinforcement Learning and Optimal Control by Dimitri},
  author={P. Bertsekas},
  year={2019}
}
This is Chapter 3 of the draft textbook “Reinforcement Learning and Optimal Control.” The chapter represents “work in progress,” and it will be periodically updated. It more than likely contains errors (hopefully not serious ones). Furthermore, its references to the literature are incomplete. Your comments and suggestions to the author at dimitrib@mit.edu are welcome. The date of last revision is given below. The date of last revision is given below. (A “revision” is any version of the chapter… Expand

Figures from this paper

Revised Progressive-Hedging-Algorithm Based Two-layer Solution Scheme for Bayesian Reinforcement Learning
TLDR
A novel two-layer solution scheme is proposed to approximate the optimal policy directly, by combining the time-decomposition based dynamic programming (DP) at the lower layer and the scenario-decomsposition based revised progressive hedging algorithm (PHA) atThe upper layer, for a type of Bayesian RL problem. Expand
Reinforcement Learning Applications
  • Yuxi Li
  • Mathematics, Computer Science
  • ArXiv
  • 2019
TLDR
An introduction to reinforcement learning and a selection of RL applications, including recommender systems, computer systems, energy, finance, healthcare, robotics, and transportation are discussed. Expand