arXiv Machine Learning July 28, 2026 By Emre \"Ozkaya, Nicolas R. Gauger

Optimal Reward Shaping: Autonomous Car Parking Case Study

Read the original on arXiv Machine Learning →

arXiv:2607. 23617v1 Announce Type: new Abstract: Designing effective reward functions for model-free reinforcement learning under non-holonomic constraints remains a persistent challenge, often resulting in severe local minima such as policy paralysis or over-conservative hazard avoidance.

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

arXiv Machine Learning

Jun 10

Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning

arXiv:2606. 10129v1 Announce Type: new Abstract: While deep Reinforcement Learning (deep-RL) has been increasingly applied to parameter control in evolutionary algorithms, rigorous theoretical analysis of parameter control remains largely restricted to single-parameter settings, owing to the difficulty of deriving effective, interpretable multi-parameter policies amenable to formal study.

By Tai Nguyen, Phong Le, Carola Doerr, Nguyen Dang

reinforcement-learning safety

arXiv Machine Learning

Jul 16

Structured Reinforcement Learning for Bayesian Persuasion : Application to Intelligent Interactive Driving

arXiv:2607. 13576v1 Announce Type: new Abstract: Interactive driving, wherein an intelligent lead vehicle equipped with real-time traffic data coordinates route choices of connected vehicles, offers a promising approach to dynamic traffic management.

By Merlin Paul, Anup Aprem

agents reinforcement-learning

arXiv Machine Learning

Jul 7

A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective

arXiv:2503. 23650v2 Announce Type: replace Abstract: Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising approach to addressing motion planning (MoP) challenges in autonomous driving (AD).

By Zhuoren Li, Guizhe Jin, Ran Yu, Weiqi Zhang, Zhiwen Chen, Nan Li, Lu Xiong, Ilya Kolmanovsky, Dimitar Filev, Bo Leng, Jia Hu

agents reinforcement-learning robotics

arXiv Machine Learning

Jul 20

Dichotomous Diffusion Policy Optimization

arXiv:2601. 00898v3 Announce Type: replace Abstract: Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inference.

By Ruiming Liang, Yinan Zheng, Kexin Zheng, Tianyi Tan, Jianxiong Li, Liyuan Mao, Zhihao Wang, Guang Chen, Hangjun Ye, Jingjing Liu, Jinqiao Wang, Xianyuan Zhan

agents diffusion reinforcement-learning robotics multimodal benchmarks

arXiv AI

1d ago

Understanding and Stabilizing Deep Q-Learning via Controlled Bootstrapping and Regulated Value Dynamics

arXiv:2608. 16182v1 Announce Type: cross Abstract: Deep Q-learning (DQL) has achieved remarkable empirical success in reinforcement learning, yet its training process remains notoriously unstable.

By Bozhou Chen, Yongyi Wang, Hanyu Liu, Xionghui Yang, Wenxin Li

reinforcement-learning safety

arXiv AI

Jul 1

Reward Redistribution for CVaR MDPs using a Bellman Operator on L-infinity

arXiv:2602. 03778v2 Announce Type: replace-cross Abstract: Tail-end risk measures such as static conditional value-at-risk (CVaR) are used in safety-critical applications to prevent rare, yet catastrophic events.

By Aneri Muni, Vincent Taboga, Esther Derman, Pierre-Luc Bacon, Erick Delage

reinforcement-learning safety

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