Rethinking Closed-loop Training for Autonomous Driving

27 June 2023

Papers citing "Rethinking Closed-loop Training for Autonomous Driving"

8 / 8 papers shown

Title
CaRL: Learning Scalable Planning Policies with Simple Rewards Bernhard Jaeger D. Dauner Jens Beißwenger Simon Gerstenecker Kashyap Chitta Andreas Geiger 54 0 0 24 Apr 2025
LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving Hao Sha Yao Mu Yuxuan Jiang Li Chen Chenfeng Xu Ping Luo Shengbo Eben Li Masayoshi Tomizuka Wei Zhan Mingyu Ding 114 159 0 04 Oct 2023
Learning to drive from a world on rails Di Chen V. Koltun Philipp Krahenbuhl 98 116 0 03 May 2021
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models Nicholas Rhinehart Jeff He Charles Packer Matthew A. Wright R. McAllister Joseph E. Gonzalez Sergey Levine 103 32 0 21 Apr 2021
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors Simon Suo S. Regalado Sergio Casas R. Urtasun 151 224 0 17 Jan 2021
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving Ming Zhou Jun Luo Julian Villela Yaodong Yang David Rusu ... H. Ammar Hongbo Zhang Wulong Liu Jianye Hao Jun Wang 139 193 0 19 Oct 2020
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Sergey Levine Aviral Kumar George Tucker Justin Fu OffRL GP 340 1,955 0 04 May 2020
BARK: Open Behavior Benchmarking in Multi-Agent Environments Julian Bernhard Klemens Esterle Patrick Hart Tobias Kessler 90 41 0 05 Mar 2020