Hybrid Action Space RL

RL: Reinforcement Learning

Latest papers

8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts
Sep 30, 2026Jingbo Wang, Wenxuan Song, Wenhao Yu +6Flow Matching Efficient VLA Model Inference
The Hong Kong University of Science and Technology (Guangzhou) · South China University of Technology · University of Science and Technology of China +3
Learning to build covering structures with continuous adjustments
Sep 8, 2026Gabriel Vallat, Maryam Kamgarpour, Stefana ParaschoRL for Robotics Robotic Construction
Knowledge-Data-Dual-Driven Reinforcement Learning for Autonomous Vehicle Control in Mixed Traffic
Aug 14, 2026Jie Fang, Wei Zheng, Mengyun Xu +1Hybrid Action Space RL RL for Autonomous Driving
department of Civil Engineering, Fuzhou University, Fuzhou, 350108, China
Revisiting Action Factorization for Complex Action Spaces
Jun 25, 2026Timothy Flavin, Sandip SenReinforcement Learning Actor-Critic Methods
The University of Tulsa, Tulsa, Oklahoma, USA
TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning
Jun 16, 2026Zijie Meng, Ziwei Li, Yufei Liu +5Cyber-Physical Systems Reinforcement Learning
Peking University · National Taiwan University · WHU +1
Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
May 14, 2026Matias Alvo, Daniel Russo, Yash KanoriaRL Control Reinforcement Learning
Graduate School of Business Columbia University
Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach
Apr 15, 2026Li-Hsiang Shen, Yu-Quan ZhengHybrid Action Space RL Wireless Communications
Department of Communication Engineering, National Central University, Taoyuan 320317, Taiwan
Hybrid TD3: Overestimation Bias Analysis and Stable Policy Optimization for Hybrid Action Space
Mar 1, 2026Thanh-Tuan Tran, Thanh Nguyen Canh, Nak Young Chong +1Hybrid Action Space RL Reinforcement Learning
University of Engineering and Technology, Vietnam National University, 10000, Hanoi, Vietnam. · School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, 923-1211, Ishikawa, Japan. · Department of Robotics, Hanyang University, Ansan, 15588, Gyeonggi, Korea.

Sep 30, 2026J/K move · Enter open · S save

Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts

Jingbo Wang, Wenxuan Song, Wenhao Yu, Han Zhao+5

The Hong Kong University of Science and Technology (Guangzhou) · South China University of Technology · University of Science and Technology of China+3

Flow Matching Efficient VLA Model Inference Robotic Manipulation Vision-Language-Action Models Hybrid Action Space RL

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Efficient action generation in vision-language-action (VLA) models requires capturing both coarse action structure and fine-grained details. Discrete action tokens provide compact structural representations but sacrifice precision, while continuous action tokens offer high precision but often require multiple denoising steps. We introduce Discrete Forcing, a flow-matching framework that combines these representations through an explicit coarse-to-fine generation process. It first predicts discrete action tokens to establish a coarse action structure, then uses them to guide continuous action refinement. The discrete and continuous components share a common diffusion transformer backbone with specialized branches, maintaining a parameter count comparable to a conventional single-branch model while requiring only one forward pass per branch. Extensive evaluations across multiple benchmarks demonstrate improved performance and faster inference over a parameter-matched continuous action expert, with consistent performance gains as model capacity increases. Real-world experiments further demonstrate improvements on high-precision and dynamic manipulation tasks.