State Entropy Regularization for Robust Reinforcement Learning

NeurIPSOral2025

Authors
Yonatan Ashlag, Uri Koren, Mirco Mutti, Esther Derman, Pierre-Luc Bacon, Shie Mannor
Affiliation
Technion - Israel Institute of Technology, Technion
Venue
NeurIPS 2025
Track
Oral

TL;DR

State entropy regularization has empirically shown better exploration and sample complexity in reinforcement learning (RL). However, its theoretical guarantees have not been studied.

Opening excerpt from the authors’ abstract. source

Read the paper

Topics

reinforcement learning sample complexity exploration

← All NeurIPS 2025 Oral papers · Browse the whole archive