arXiv Machine Learning
Sep 23

ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy

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.

By Hongming Li, Zhao Yang, Xiaoxuan Liang, Shujian Yu, Jose C. Principe
arXiv AI
Jun 6

Retry Policy Gradients in Continuous Action Spaces

arXiv:2606. 05888v1 Announce Type: new Abstract: Retry-based objectives such as pass@K and max@K optimize the best return obtained from multiple sampled trajectories, and recent work has shown that they can promote exploration without explicit exploration bonuses.

By Soichiro Nishimori, Paavo Parmas