arXiv Machine Learning By Senne Deproost, Mehrdad Asadi, Ann Now\'e

Hierarchical Support Vector State Partitioning for Distilling Black Box Reinforcement Learning Policies

Read the original on arXiv Machine Learning →

arXiv:2605. 04254v3 Announce Type: replace Abstract: We introduce State Vector Space Partitioning (SVSP), a novel method to mimic a black box reinforcement learning policy using a set of human-interpretable subpolicies.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
Jun 10

Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning

arXiv:2606. 10613v1 Announce Type: cross Abstract: Diffusion-based Q-learning has emerged as a powerful paradigm for offline reinforcement learning, but its reliance on multi-step denoising makes both training and inference computationally expensive and brittle.

By Thanh Nguyen, Tri Ton, Hongbin Choe, Tung M. Luu, Chang D. Yoo