Simulation-Based RL

RL: Reinforcement Learning

Latest papers

21 papers5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Reinforcement Learning for Adaptive Crowd Flow Control Using Dynamic Barriers
Oct 25, 2026Alhanof Alolyan, He Wang, Amani AlonayzanSimulation-Based RL RL Control
Princess Nourah bint Abdulrahman University, Saudi Arabia · University College London, United Kingdom · Independent Researcher
Regularised RL improves sim-to-real transfer
Oct 23, 2026Danilo Vucetic, Mohamad H. Danesh, Florence Clerc +2Regularized RL Reinforcement Learning
Université de Montréal · Mila - Québec AI Institute · McGill University +2
Active Generative Simulation: Meta-Learning What to Simulate for Sim-and-Real Policy Co-Training
Oct 14, 2026Liqian Ma, Vaibhav Saxena, Utkarsh Aashu Mishra +3Synthetic Data Generation for Robotics Sim-to-Real Transfer
Georgia Institute of Technology
A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control
Oct 8, 2026Octi Zhang, Mateo Guaman Castro, Patrick Yin +4Robotic RL Simulation-Based RL
University of Washington · NVIDIA
Exact Fast Batch Simulation for Tabular Reinforcement Learning
Oct 3, 2026Haochen Zhang, Lingzhou Xue, Zhong ZhengSimulation-Based RL Tabular RL
Department of Statistics, The Pennsylvania State University · Department of Applied and Computational Mathematics and Statistics, University of Notre Dame
WTF?! Simulation-Free Reinforcement Learning with Wasserstein-Tilted Flow Maps
Sep 22, 2026Abbas Mammadov, Jerry Y. Huang, Justin Lin +5Simulation-Based RL Reinforcement Learning
University of Oxford · Carnegie Mellon University
Provably Safe Sim-to-Real Transfer
Sep 1, 2026Tingting Ni, Maryam KamgarpourReinforcement Learning Sim-to-Real Transfer
SYCAMORE, EPFL
Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning
Jul 6, 2026Saurabhsingh Rajput, Tushar SharmaRL for Code Generation Simulation-Based RL
Dalhousie University, Halifax, Canada
Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy
Jun 26, 2026Matthew Vandergrift, Esraa Elelimy, Martha WhiteRL Benchmarks Simulation-Based RL
Department of Computing Science, University of Alberta · Alberta Machine Intelligence Institute (Amii) · Canada CIFAR AI Chair
Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
Jun 25, 2026Raymond Yu, William Huey, Mustafa Mukadam +2Robotic Manipulation RL for Robotics
University of Washington · Amazon FAR
HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning
Jun 7, 2026Zechu Li, Yufeng Jin, Xiaoyang Liu +4Agentic RL Reinforcement Learning
TU Darmstadt 1 · Hessian.AI 7 · Honda Research Institute Europe 2 +4
Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning
May 27, 2026Christoph Dann, Yishay Mansour, Mehryar MohriSim-to-Real Transfer Active Learning
Google Research · Tel Aviv University and Google Research · Google Research and Courant Institute of Mathematical Sciences
Accelerating Reinforcement Learning Training Using Simulation Surrogate Models
May 26, 2026Mohammadmahdi Ghasemloo, David J. Eckman, Yaxian LiSurrogate Modeling Reinforcement Learning
Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA · Intuit AI, Mountain View, CA, USA
HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL
May 18, 2026Zhi Li, Songkun Yan, Jie Cao +4Simulation-Based RL Reinforcement Learning
Civil, Environmental, and Architectural Engineering, University of Colorado Boulder · Civil Engineering and Environmental Sciences, University of Oklahoma · Department of Computer Science, University of Oklahoma +3
On Training in Imagination
May 7, 2026Nadav Timor, Ravid Shwartz-Ziv, Micah Goldblum +2Reward Modeling Reinforcement Learning
Weizmann Institute of Science · New York University · Columbia University
Discovering Reinforcement Learning Interfaces with Large Language Models
May 5, 2026Akshat Singh Jaswal, Ashish Baghel, Paras ChopraReinforcement Learning LLM-Guided RL
Lossfunk
Democratizing Tool Learning with Environments Fully Simulated by a Free 8B Language Model
Apr 20, 2026Chenming Tang, Hsiu-Yuan Huang, Weijie Liu +3LLM Agent Training RL for Tool Use
National Key Laboratory for Multimedia Information Processing, Peking University · School of Computer Science, Peking University · LLM Department, Tencent
Abstract Sim2Real through Approximate Information States
Apr 16, 2026Yunfu Deng, Yuhao Li, Josiah P. HannaSim-to-Real Transfer State Abstraction
Department of Computer Sciences, University of Wisconsin–Madison, Madison, WI 53706 USA · Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA 01003 USA
Automatic Generation of High-Performance RL Environments
Mar 12, 2026Seth Karten, Rahul Dev Appapogu, Chi JinSimulation-Based RL Reinforcement Learning
1Princeton University · 2Independent Researcher
Phys4D: Fine-Grained Physics-Consistent 4D Modeling from Video Diffusion
Mar 3, 2026Haoran Lu, Shang Wu, Songling Liu +9Simulation-Based RL Physical Consistency in Video Generation
1Northwestern University · 2Dolby Laboratories
An Real-Sim-Real (RSR) Loop Framework for Generalizable Robotic Policy Transfer
Mar 13, 2025Yuxuan Xu, Shiyu Wang, Jinhao Huang +6Robotic Data Collection Sim-to-Real Transfer
Institute for AI Industry Research (AIR), Tsinghua University · Beijing Jiaotong University · The Hong Kong University of Science and Technology (Guangzhou)

Oct 25, 2026J/K move · Enter open · S save

Reinforcement Learning for Adaptive Crowd Flow Control Using Dynamic Barriers

Alhanof Alolyan, He Wang, Amani Alonayzan

Princess Nourah bint Abdulrahman University, Saudi Arabia · University College London, United Kingdom · Independent Researcher

Simulation-Based RL RL Control Reinforcement Learning

Open paper AskPDF arXiv

Crowd barriers are widely used to regulate pedestrian movement during large public events such as pilgrimages. However, fixed barrier layouts often create unnecessary stopping and local congestion even when they successfully control the overall crowd flow. Finding better barrier configurations is difficult because there is no dataset that provides the best barrier placement for a given crowd scene. We address this challenge by introducing a simulation-driven reinforcement learning framework that learns barrier configurations without requiring labelled training data. Starting from a segmented crowd scene, we construct a realistic simulation environment that reproduces the observed pedestrian distribution. This environment is then used to train a Soft Actor-Critic (SAC) agent that continuously adjusts barrier positions to improve pedestrian movement. Unlike supervised approaches the proposed framework allows the agent to explore barrier configurations that have never been observed in real data. We evaluate the learned policy under unseen numbers of pedestrians. The learned policy consistently outperforms the existing manually designed barrier configuration by reducing pedestrian stops while maintaining effective flow regulation. These results demonstrate that combining computer vision crowd simulation and reinforcement learning provides an effective framework for optimizing crowd control infrastructure.