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WiseMove: A Framework to Investigate Safe Deep Reinforcement Learning for Autonomous Driving

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Quantitative Evaluation of Systems (QEST 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11785))

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WiseMove is a platform to investigate safe deep reinforcement learning (DRL) in the context of motion planning for autonomous driving. It adopts a modular architecture that mirrors our autonomous vehicle software stack and can interleave learned and programmed components. Our initial investigation focuses on a state-of-the-art DRL approach from the literature, to quantify its safety and scalability in simulation, and thus evaluate its potential use on our vehicle.

J. Lee, A. Balakrishnan, A. Gaurav and S. Sedwards—Contributed equally.

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    Details and scripts to reproduce our results can be found in our repository (see Footnote 3).


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This work is supported by the Japanese Science and Technology agency (JST) ERATO project JPMJER1603: HASUO Metamathematics for Systems Design, and by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant: Model-Based Synthesis and Safety Assurance of Intelligent Controllers for Autonomous Vehicles.

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Correspondence to Krzysztof Czarnecki .

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Lee, J., Balakrishnan, A., Gaurav, A., Czarnecki, K., Sedwards, S. (2019). WiseMove: A Framework to Investigate Safe Deep Reinforcement Learning for Autonomous Driving. In: Parker, D., Wolf, V. (eds) Quantitative Evaluation of Systems. QEST 2019. Lecture Notes in Computer Science(), vol 11785. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30280-1

  • Online ISBN: 978-3-030-30281-8

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