让自动驾驶车道保持自动学习权重，无需人工调参。
CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving
- 将车道保持建模为约束强化学习，自动优化权重和策略
- 实测效率与可靠性优于传统强化学习方法
- 已在真实场景验证，适合实际自动驾驶系统部署
自动驾驶中的车道保持需要针对不同场景手动调整目标权重。本文将车道保持问题建模为约束强化学习，使权重系数与策略一同自动学习，无需针对场景进行人工调参。实验表明，该方法在效率和可靠性上均优于传统强化学习。此外，真实世界演示验证了其在实际自动驾驶中的实用价值。
原文摘要 · Abstract (English)
Lane keeping in autonomous driving systems requires scenario-specific weight tuning for different objectives. We formulate lane-keeping as a constrained reinforcement learning problem, where weight coefficients are automatically learned along with the policy, eliminating the need for scenario-specific tuning. Empirically, our approach outperforms traditional RL in efficiency and reliability. Additionally, real-world demonstrations validate its practical value for real-world autonomous driving.
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