• Corpus ID: 244478308

UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning

@article{Diehl2021UMBRELLAUM,
  title={UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning},
  author={Christopher P. Diehl and Timo Sievernich and Martin Kr{\"u}ger and Frank Hoffmann and Torsten Bertram},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.11097}
}
Offline reinforcement learning (RL) provides a framework for learning decisionmaking from offline data and therefore constitutes a promising approach for realworld applications as automated driving. Self-driving vehicles (SDV) learn a policy, which potentially even outperforms the behavior in the sub-optimal data set. Especially in safety-critical applications as automated driving, explainability and transferability are key to success. This motivates the use of model-based offline RL approaches… 

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