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arXiv Machine Learning By Hongming Li, Zhao Yang, Xiaoxuan Liang, Shujian Yu, Jose C. Principe

ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy

Read the original on arXiv Machine Learning →

The paper introduces ELEMENT, a framework that combines episodic and lifelong entropy maximization to drive reward-free exploration in reinforcement learning. It addresses two key limitations of existing entropy-based methods: the vanishing intrinsic reward after a state is visited and the computational cost of estimating entropy over large datasets. ELEMENT achieves this by deriving an average episodic state entropy reward and employing a k‑NN graph‑based estimator for lifelong entropy, leading to superior state coverage and unsupervised pre‑training performance compared to current baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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