Reinforcement learning (RL) is a powerful data-driven control method that has been largely explored in autonomous driving tasks . However, conventional RLapproaches learn control policies through trial-and-error interactions with theenvironment . Offline RL has recently emerged as a promisingframework to learn effective policies from previously-collected, staticdatasets without the requirement of active interactions, making it especially appealing for autonomous driving applications . This paper presents an enhanced BCQ algorithm by employing a learnable parameter noise scheme in the perturbation model to increase the diversity of observed actions . In addition, a Lyapunov-based safety enhancement strategy is incorporated to constrain the explorable state space within a saferegion . Experimental results in highway and parking traffic scenarios show that

Author(s) : Tianyu Shi, Dong Chen, Kaian Chen, Zhaojian Li

Links : PDF - Abstract

Code :
Coursera

Keywords : driving - autonomous - enhancement - safety - interactions -

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