Web: http://arxiv.org/abs/2206.08528

June 20, 2022, 1:10 a.m. | Linrui Zhang, Qin Zhang, Li Shen, Bo Yuan, Xueqian Wang

cs.LG updates on arXiv.org arxiv.org

Safe reinforcement learning (RL) has achieved significant success on
risk-sensitive tasks and shown promise in autonomous driving (AD) as well.
Considering the distinctiveness of this community, efficient and reproducible
baselines are still lacking for safe AD. In this paper, we release SafeRL-Kit
to benchmark safe RL methods for AD-oriented tasks. Concretely, SafeRL-Kit
contains several latest algorithms specific to zero-constraint-violation tasks,
including Safety Layer, Recovery RL, off-policy Lagrangian method, and Feasible
Actor-Critic. In addition to existing approaches, we propose a novel …

arxiv autonomous autonomous driving driving learning lg reinforcement reinforcement learning

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