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