Primal-Dual Algorithms for Learning in Constrained Markov Decision Processes

• Author / Creator

  Liu, Chang

• Many real-world tasks in fields such as robotics and control can be formulated as constrained Markov decision processes (CMDPs). In CMDPs, the objective is usually to optimize the return while ensuring some constraints being satisfied at the same time. The primal-dual approach is a common technique of addressing CMDPs. It rewrites the original optimization problem of CMDPs into its equivalent Lagrangian form. In this thesis, we deliver an overview of CMDPs and the primal-dual approach, explain several algorithm designs adopting the primal-dual approach under different learning settings in terms of simulator types, and provide analysis of these algorithms.

• Subjects / Keywords

  • Reinforcement Learning
  • Constrained Markov Decision Process
  • Primal-dual Algorithms
  • Learning theory

• Graduation date

  Fall 2023

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-a06h-5c25

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.