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Understanding State Abstractions in RL by zzyy20010204 is a document available to read on EtoBox.

What is Understanding State Abstractions in RL about?

The document discusses state abstraction as a method for generalizing learning in large state spaces, emphasizing its role in increasing effective sample size while balancing approximation and estimation errors. It defines a hierarchy of state abstractions, from exact to approximate, and outlines the implications of each type on reinforcement learning algorithms. The document also highlights the challenges of finding exact abstractions and the advantages of model-irrelevance in planning and learning context

Author

zzyy20010204

Language

EN