arXiv:2606. 15301v1 Announce Type: cross Abstract: The Lenstra-Lenstra-Lov\'asz (LLL) algorithm is a seminal contribution to computer science used for lattice basis reduction, yet its polynomial-time outputs produce bases that are far from optimal as the dimension grows.
By Mohamed Malhou, Kristin Lauter, Ludovic Perret
The paper introduces a method that integrates action abstraction into policy optimization for reinforcement learning and generative flow networks. By iteratively identifying frequently used action subsequences in high‑reward trajectories and treating them as single high‑level actions, the approach expands the action space and improves sample efficiency. Experiments on synthetic and real‑world tasks show that this technique discovers diverse high‑reward states more effectively, especially on challenging exploration problems, and yields interpretable abstract actions that reflect the underlying reward structure.
By Oussama Boussif, L\'ena N\'ehale Ezzine, Joseph D Viviano, Micha{\l} Koziarski, Moksh Jain, Esmeralda S. Whitammer, Emmanuel Bengio, Rim Assouel, Yoshua Bengio
arXiv:2606. 17386v1 Announce Type: cross Abstract: End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments.
By Zikang Xiong, Weixin Li, Zhouchonghao Wu, Akshay Rangesh, Saarth Bonde, Grantland Hall, Chen Tang, Yihan Hu, Wei Zhan
arXiv:2605.08982v4 Announce Type: replace
Abstract: Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement and action selection in Reinforcement Learning. Due to its sequenti...
By Yaniv Oren, Viliam Vadocz, Joery A. de Vries, Wendelin B\"ohmer, Matthijs T. J. Spaan, Hendrik Baier
Selective Growing Q‑Networks (SGQN) extend value‑based Q‑learning for continuous control by replacing uniform action‑grid growth with an adaptive grid that adds bins near high‑Q regions and prunes low‑Q bins, while maintaining decoupled Q‑learning and epsilon‑greedy action selection. On eight DeepMind Control Suite tasks, SGQN achieves the highest mean final return on four tasks, matches the best method within 1.1% on three more, and uses on average 7.5 of 9 available bins per dimension. Experiments show that the adaptive grid’s growth and pruning have limited impact on performance, whereas starting from a coarse grid and using epsilon‑greedy selection are critical for success.
By Parham Nooranbakht, Seyed Mahdi Basiri Azad, Joschka B\"odecker
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