Copertina

RIFT: Group-Relative RL Fine-Tuning for Realistic and Controllable Traffic Simulation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Keyu, Sun, Wenchao, Cheng, Hao, Zheng, Sifa
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Robotics Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

ForSim: Stepwise Forward Simulation for Traffic Policy Fine-Tuning
di: Chen, Keyu, et al.
Pubblicazione: (2026)

CRAFT: Counterfactual-to-Interactive Reinforcement Fine-Tuning for Driving Policies
di: Chen, Keyu, et al.
Pubblicazione: (2026)

FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality
di: Chen, Keyu, et al.
Pubblicazione: (2024)

Heterogeneous Self-Play for Realistic Highway Traffic Simulation
di: Qiu, Jinkai, et al.
Pubblicazione: (2026)

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning
di: Yin, Patrick, et al.
Pubblicazione: (2025)

Safety-Critical Traffic Simulation with Adversarial Transfer of Driving Intentions
di: Huang, Zherui, et al.
Pubblicazione: (2025)

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only
di: Xiao, Wei, et al.
Pubblicazione: (2025)

VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning
di: Huang, Binghao, et al.
Pubblicazione: (2025)

Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models
di: Lyu, Mingyang, et al.
Pubblicazione: (2025)

An Efficient Detection and Control System for Underwater Docking using Machine Learning and Realistic Simulation: A Comprehensive Approach
di: Chavez-Galaviz, Jalil, et al.
Pubblicazione: (2023)

Rolling Ahead Diffusion for Traffic Scene Simulation
di: Liu, Yunpeng, et al.
Pubblicazione: (2025)

DecompGAIL: Learning Realistic Traffic Behaviors with Decomposed Multi-Agent Generative Adversarial Imitation Learning
di: Guo, Ke, et al.
Pubblicazione: (2025)

Per-Group Error, Not Total MSE: Fine-Tuning Vision-Language-Action Models for 11-DoF Mobile Manipulation
di: Bofi, Pau Montagut, et al.
Pubblicazione: (2026)

Active Fine-Tuning of Multi-Task Policies
di: Bagatella, Marco, et al.
Pubblicazione: (2024)

RL from Physical Feedback: Aligning Large Motion Models with Humanoid Control
di: Yue, Junpeng, et al.
Pubblicazione: (2025)

EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning
di: Poudel, Bibek, et al.
Pubblicazione: (2023)

Performance Comparison of Deep RL Algorithms for Mixed Traffic Cooperative Lane-Changing
di: Yao, Xue, et al.
Pubblicazione: (2024)

RIFT: Repurposing Negative Samples via Reward-Informed Fine-Tuning
di: Liu, Zehua, et al.
Pubblicazione: (2026)

On the Design of Safe Continual RL Methods for Control of Nonlinear Systems
di: Coursey, Austin, et al.
Pubblicazione: (2025)

SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows
di: Yang, Chenyu, et al.
Pubblicazione: (2026)

A Heuristic Approach for Performance Tuning in RL-based Quadrotor Control via Reward Design and Termination Conditions
di: Suarez, Fausto Mauricio Lagos, et al.
Pubblicazione: (2026)

Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
di: Wagenmaker, Andrew, et al.
Pubblicazione: (2024)

Inverse RL Scene Dynamics Learning for Nonlinear Predictive Control in Autonomous Vehicles
di: Grigorescu, Sorin, et al.
Pubblicazione: (2025)

Momentum Based Reward Design for Low Emission Traffic Signal Control
di: Mundane, Chinmay, et al.
Pubblicazione: (2026)

From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning
di: Sun, Zhanyi, et al.
Pubblicazione: (2026)

What Matters in Learning A Zero-Shot Sim-to-Real RL Policy for Quadrotor Control? A Comprehensive Study
di: Chen, Jiayu, et al.
Pubblicazione: (2024)

RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning
di: Ahmadi, Ehsan, et al.
Pubblicazione: (2026)

RL Token: Bootstrapping Online RL with Vision-Language-Action Models
di: Xu, Charles, et al.
Pubblicazione: (2026)

CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control
di: Zhang, Yifeng, et al.
Pubblicazione: (2026)

Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantization
di: Guo, Jian-Ting, et al.
Pubblicazione: (2025)

RoDiF: Robust Direct Fine-Tuning of Diffusion Policies with Corrupted Human Feedback
di: Vatsa, Amitesh, et al.
Pubblicazione: (2026)

Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation
di: Son, Sanghyun, et al.
Pubblicazione: (2024)

GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation
di: Li, Yunfei, et al.
Pubblicazione: (2025)

Mixed Traffic Control and Coordination from Pixels
di: Villarreal, Michael, et al.
Pubblicazione: (2023)

Fisher-Preserving Guidance: Training-Free Manifold Constraints for Safe Diffusion Control
di: Ren, Hao, et al.
Pubblicazione: (2026)

RL-based Control of UAS Subject to Significant Disturbance
di: Chakraborty, Kousheek, et al.
Pubblicazione: (2025)

Modality-Augmented Fine-Tuning of Foundation Robot Policies for Cross-Embodiment Manipulation on GR1 and G1
di: Park, Junsung, et al.
Pubblicazione: (2025)

CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning
di: Huang, Dongchi, et al.
Pubblicazione: (2025)

Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement Learning
di: Hao, Chenjie, et al.
Pubblicazione: (2025)

Constrained Group Relative Policy Optimization
di: Girgis, Roger, et al.
Pubblicazione: (2026)