Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes

Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes

Simons Institute via YouTube Direct link

Assumptions on policies

14 of 16

14 of 16

Assumptions on policies

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Classroom Contents

Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes

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  1. 1 Intro
  2. 2 Questions of interest
  3. 3 Main challenges
  4. 4 MDP Preliminaries
  5. 5 Policy parameterizations
  6. 6 Policy gradient algorithm
  7. 7 Policy gradient example: Softmax parameterization
  8. 8 Entropy regularization
  9. 9 Convergence of Entropy regularized PG
  10. 10 A natural solution
  11. 11 Proof ideas
  12. 12 Restricted parameterizations
  13. 13 A closer look at Natural Policy Gradient • NPG performs the update
  14. 14 Assumptions on policies
  15. 15 Extension to finite samples
  16. 16 Looking ahead

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