RL HPO

HPO: Hyperparameter Optimization · RL: Reinforcement Learning

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10 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Hyperparameter and Neural Architecture Search via Evolutionary Optimization for Reinforcement Learning: HPNAS-RL
Oct 27, 2026Nihal Acharya Adde, Hanno GottschalkRL HPO Neural Architecture Search
Volkswagen Group Innovation, Volkswagen AG, Berliner Ring 2, Wolfsburg, 38440, Lower Saxony, Germany. · Institute of Mathematics, TU Berlin, Straße des 17. Juni 136, Berlin, 10623, Berlin, Germany.
REDAR: Reinforcement-Driven Adaptive Regularisation for Class Incremental Learning
Oct 22, 2026Akila Ayanthi, Darshana Priyasad, Tharindu Fernando +3RL HPO Class-Incremental Learning
SAIVT, Queensland University of Technology, Australia · CSIRO Robotics, CSIRO, Australia
FSPO: Policy-Consistent Risk and Pareto-Feasible Control for Budgeted LLM RL Post-Training
Oct 2, 2026Miaobo Hu, Shuhao Hu, Xiaobo Guo +3RL HPO Reinforcement Learning
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
TRACE-CASH: Trial-History-Conditioned Reinforcement Learning for Adaptive Configuration Exploration in Time-Series CASH
Aug 17, 2026Yu-Han Huang, Yujia Wu, Vincent S. TsengRL HPO Hyperparameter Optimization
Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
Jul 29, 2026Mingxuan Che, Tsung-Yuan Tseng, Theresa Eimer +2Bayesian Optimization Risk-Sensitive RL
Leibniz University Hannover · CARIAD SE · University of Wuppertal +2
Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners
Jul 14, 2026Haseeb Shah, Lingwei Zhu, Adam White +1RL Control Reinforcement Learning
Department of Computing Science, University of Alberta, Canada · Alberta Machine Intelligence Institute (amii), Canada · Great Bay University, China +1
LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents
Jun 16, 2026Haoyang Fang, Wei Zhu, Boran Han +11Reinforcement Learning Large Language Model-Guided Optimization
†LLMZero Project Core Team. · ∗Work done at Amazon.
Efficient Hyperparameter Optimization for LLM Reinforcement Learning
Jun 2, 2026Minping Chen, Bowen Xiao, Du Liang +2RL for Language Models RL Post-Training
1The Hong Kong University of Science and Technology (Guangzhou) · 3China United Network Communications Group · 2The Hong Kong University of Science and Technology
Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters
May 4, 2026Lingxiao Kong, Cong Yang, Oya Deniz Beyan +1RL for Robotics Reinforcement Learning
Fraunhofer Institute for Applied Information Technology FIT, Germany · Soochow University, China · University of Cologne, Germany +1
When Learning Rates Go Wrong: Early Structural Signals in PPO Actor-Critic
Mar 10, 2026Alberto Fernández-Hernández, Cristian Pérez-Corral, Jose I. Mestre +3RL HPO Reinforcement Learning
Universitat Polit`ecnica de Val`encia, Valencia, Spain · Universitat Jaume I, Castell´o de la Plana, Spain · Openchip & Software Technologies, Barcelona, Spain

Oct 27, 2026J/K move · Enter open · S save

Hyperparameter and Neural Architecture Search via Evolutionary Optimization for Reinforcement Learning: HPNAS-RL

Nihal Acharya Adde, Hanno Gottschalk

Volkswagen Group Innovation, Volkswagen AG, Berliner Ring 2, Wolfsburg, 38440, Lower Saxony, Germany. · Institute of Mathematics, TU Berlin, Straße des 17. Juni 136, Berlin, 10623, Berlin, Germany.

RL HPO Neural Architecture Search Reinforcement Learning RL for Autonomous Driving Evolutionary Optimization

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In this study, we propose a dual-branch evolutionary framework that simultaneously performs Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO) for Reinforcement Learning (RL) in Autonomous Driving (AD) tasks. While NAS has shown strong potential in automating architecture design for Convolutional Neural Networks (CNNs), its application to RL remains limited, especially in scenarios that require joint optimization of architectural and training dynamics. Building on our prior work on Evolutionary Multi-Objective NAS (EMNAS), the proposed framework evolves discrete network architectures and continuous hyperparameters in parallel under a unified multi-objective search strategy. In this setup, NAS targets the convolutional layers of the RL policy network - responsible for visual feature extraction, while HPO focuses on optimizing critical RL hyperparameters. To accelerate convergence and improve generalization, we incorporate parallel evaluation and a teacher-student policy transfer mechanism. Experiments in a high-fidelity driving simulator show that the joint NAS-HPO framework consistently outperforms standalone NAS, yielding policies with higher rewards, fewer parameters, and improved stability. These results highlight the benefits of jointly optimizing model structure and training dynamics for scalable and effective RL agent design.