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What is Structural Information in Reinforcement Learning about?

This paper introduces a novel framework for Reinforcement Learning (RL) called SIDM, which leverages Structural Information principles to enhance decision-making in noisy and high-dimensional environments. The framework employs unsupervised partitioning methods for state and action spaces, hierarchical abstractions, and a two-layer skill-based learning mechanism, significantly improving policy quality, stability, and sample efficiency compared to state-of-the-art algorithms. Extensive evaluations demonstrat

Author

qianhaodong12

Language

EN