Efficient exploration in high-dimensional domains with unknown feature embeddings is crucial for reinforcement learning. The VoX algorithm introduces a barycentric spanner approach to facilitate exploration while interleaving representation learning and policy optimization efficiently. By overcoming computational challenges and avoiding restrictive assumptions, VoX provides a promising solution for efficient exploration in Low-Rank MDPs.
The content discusses the complexities of designing algorithms for Low-Rank MDPs, highlighting the need for efficient, model-free solutions. It addresses challenges related to computational efficiency, model-based approaches, and strong structural assumptions that existing algorithms face. The proposed VoX algorithm aims to provide a sample-efficient method for exploration by leveraging barycentric spanners and robust representation learning objectives.
Key points include the importance of efficient exploration strategies in reinforcement learning, the limitations of existing algorithms due to computational complexity or restrictive assumptions, and the innovative approach of VoX using barycentric spanners for exploration efficiency.
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by Zakaria Mham... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2307.03997.pdfDeeper Inquiries