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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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Abstract

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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Notes

  1. 1.

    uwaterloo.ca/waterloo-intelligent-systems-engineering-lab/.

  2. 2.

    therecord.com/news-story/8859691-waterloo-s-autonomoose-hits-100-kilometre-milestone/.

  3. 3.

    git.uwaterloo.ca/wise-lab/wise-move/.

  4. 4.

    gym.openai.com.

  5. 5.

    keras.io.

  6. 6.

    github.com/keras-rl/keras-rl.

  7. 7.

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

References

  1. Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning (2015). http://arxiv.org/abs/1509.02971

  2. Mnih, V., et al.: Playing Atari with deep reinforcement learning (2013). http://arxiv.org/abs/11312.5602

  3. Paden, B., Čáp, M., Yong, S.Z., Yershov, D., Frazzoli, E.: A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans. Intell. Veh. 1(1), 33–55 (2016)

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  4. Paxton, C., Raman, V., Hager, G.D., Kobilarov, M.: Combining neural networks and tree search for task and motion planning in challenging environments. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 6059–6066 (2017)

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  5. Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)

    Article  Google Scholar 

  6. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)

    MATH  Google Scholar 

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Acknowledgment

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. https://doi.org/10.1007/978-3-030-30281-8_20

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  • DOI: https://doi.org/10.1007/978-3-030-30281-8_20

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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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