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Autonomous Driving Based on Approximate Safe Action
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PRE
AI
DOI：10.1109/TITS.2023.3292253.png)
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摘要
En 中文
Safety limits the application of traditional reinforcement learning (RL) methods to autonomous driving. To address the challenge of safe exploration in autonomous driving tasks, a novel safe RL method called Twin Delayed Deep Deterministic Policy Gradient based on Approximate Safe Action (TD3-ASA) is proposed in this paper. In TD3-ASA, the action output by the current policy during the exploration process is modified to obtain an approximate safe action, and then the approximate safe action is utilized to train a safe policy for deployment. TD3-ASA offers several advantages: 1) TD3-ASA is sample efficient and does not need any prior knowledge; 2) TD3-ASA enhances safety both during training and deployment; 3) TD3-ASA introduces an adjustable safety correction factor that enables a tradeoff between exploration and safety. Experimental results conducted on both the MetaDrive and SpeedLimit autonomous driving test platforms demonstrate the effectiveness of TD3-ASA. TD3-ASA exhibits more than triple safety during training on MetaDrive compared to the current state-of-the-art RL method, achieving a high success rate and low deployment risk.
Keyword:
Autonomous driving
approximate safe action
safe reinforcement learning
safety correction factor
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
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