Reinforcement Learning in Feature Space: Complexity and Regret
Explore reinforcement learning in feature space through this 45-minute lecture by Mengdi Wang from Princeton University. Delve into Markov decision processes, sample complexity, and regret analysis for tabular MDP. Examine the Bellman equation, state feature maps, and value function representation using linear combinations of features. Investigate episodic reinforcement learning, Hilbert space embedding of transition kernels, and the MatrixRL algorithm. Consider the pros and cons of using features for RL and discuss potential good state features. Learn about metastable state clusters, unsupervised state aggregation, and see applications in stochastic diffusion processes and NYC taxi data. Gain insights into emerging challenges in deep learning through this Simons Institute talk.