title: RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based
    Reinforcement Learning
summary: Existing end-to-end autonomous driving (AD) algorithms typically follow the
  Imitation Learning (IL) paradigm, which faces challenges such as causal
  confusion and the open-loop gap. In this work, we establish a 3DGS-based
  closed-loop Reinforcement Learning (RL) training paradigm. By leveraging 3DGS
  techniques, we construct a photorealistic digital replica of the real physical
  world, enabling the AD policy to extensively explore the state space and learn
  to handle out-of-distribution scenarios through large-scale trial and error. To
  enhance safety, we design specialized rewards that guide the policy to
  effectively respond to safety-critical events and understand real-world causal
  relationships. For better alignment with human driving behavior, IL is
  incorporated into RL training as a regularization term. We introduce a
  closed-loop evaluation benchmark consisting of diverse, previously unseen 3DGS
  environments. Compared to IL-based methods, RAD achieves stronger performance
  in most closed-loop metrics, especially 3x lower collision rate. Abundant
  closed-loop results are presented at https://hgao-cv.github.io/RAD.
paper_authors._id: 67b55c7fba22c1ddbb8d5746
paper_authors.name: Hao Gao
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paper_authors.fullname: Hao Gao
paper_authors.type: user
paper_authors.user: Hao605
paper_authors._id: 67b55c7fba22c1ddbb8d5747
paper_authors.name: Shaoyu Chen
paper_authors.status: admin_assigned
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paper_authors.type: user
paper_authors.user: Atan-0221
paper_authors._id: 67b55c7fba22c1ddbb8d5748
paper_authors.name: Bo Jiang
paper_authors._id: 67b55c7fba22c1ddbb8d5749
paper_authors.name: Bencheng Liao
paper_authors._id: 67b55c7fba22c1ddbb8d574a
paper_authors.name: Yiang Shi
paper_authors._id: 67b55c7fba22c1ddbb8d574b
paper_authors.name: Xiaoyang Guo
paper_authors._id: 67b55c7fba22c1ddbb8d574c
paper_authors.name: Yuechuan Pu
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paper_authors.name: Haoran Yin
paper_authors._id: 67b55c7fba22c1ddbb8d574e
paper_authors.name: Xiangyu Li
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paper_authors.fullname: xiangyu
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paper_authors.user: xiangyuli
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paper_authors.name: Xinbang Zhang
paper_authors._id: 67b55c7fba22c1ddbb8d5750
paper_authors.name: Ying Zhang
paper_authors._id: 67b55c7fba22c1ddbb8d5751
paper_authors.name: Wenyu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T16:05:33.229Z
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paper_authors.fullname: LWY
paper_authors.type: user
paper_authors.user: wenyuliu
paper_authors._id: 67b55c7fba22c1ddbb8d5752
paper_authors.name: Qian Zhang
paper_authors._id: 67b55c7fba22c1ddbb8d5753
paper_authors.name: Xinggang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T16:05:15.793Z
paper_authors._id: 62600de6d47e3dbae32ce1ce
paper_authors.avatarUrl: /avatars/a536417cfec6e10ac415091bd1829426.svg
paper_authors.fullname: Xinggang Wang
paper_authors.type: user
paper_authors.user: xinggangw
paper_title: RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based
    Reinforcement Learning
paper_summary: Existing end-to-end autonomous driving (AD) algorithms typically follow the
  Imitation Learning (IL) paradigm, which faces challenges such as causal
  confusion and the open-loop gap. In this work, we establish a 3DGS-based
  closed-loop Reinforcement Learning (RL) training paradigm. By leveraging 3DGS
  techniques, we construct a photorealistic digital replica of the real physical
  world, enabling the AD policy to extensively explore the state space and learn
  to handle out-of-distribution scenarios through large-scale trial and error. To
  enhance safety, we design specialized rewards that guide the policy to
  effectively respond to safety-critical events and understand real-world causal
  relationships. For better alignment with human driving behavior, IL is
  incorporated into RL training as a regularization term. We introduce a
  closed-loop evaluation benchmark consisting of diverse, previously unseen 3DGS
  environments. Compared to IL-based methods, RAD achieves stronger performance
  in most closed-loop metrics, especially 3x lower collision rate. Abundant
  closed-loop results are presented at https://hgao-cv.github.io/RAD.
paper_ai_summary: 3DGS-based closed-loop reinforcement learning for autonomous driving integrates photorealistic simulation and specialized rewards, reducing collision rates compared to imitation learning.
paper_ai_keywords: Reinforcement Learning Imitation Learning 3DGS photorealistic digital replica state space out-of-distribution scenarios safety-critical events causal relationships closed-loop evaluation benchmark autonomous driving