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Classroom Contents
On the Statistical Complexity of Reinforcement Learning
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- 1 Intro
- 2 Tabular Markov decision process
- 3 Prior efforts: algorithms and sample complexity results
- 4 Minimax optimal sample complexity of tabular MDP
- 5 Adding some structure: state feature map
- 6 Representing value function using linear combination of features
- 7 Rethinking Bellman equation
- 8 Reducing Bellman equation using features
- 9 Sample complexity of RL with features
- 10 Of-Policy Policy Evaluation (OPE)
- 11 OPE with function approximation
- 12 Equivalence to plug-in estimation
- 13 Minimax-optimal batch policy evaluation
- 14 Lower Bound Analysis
- 15 Episodic Reinforcement Learning
- 16 Feature space embedding of transition kernel
- 17 Regret Analysis
- 18 Exploration with Value-Targeted Regression VTAL
