Abstract
The task of building unmanned automated vehicle (UAV) control systems is developing in the direction of complication of options for interaction of UAV with the environment and approaching real life situations. A new concept of so called “smart city” was proposed and view of transportation shifted in direction to self-driving cars. In this work we developed a solution to car’s movement on road intersection. For that we made a new environment to simulate a process and applied a hierarchical reinforcement learning method to get a required behaviour from a car. Created environment could be then used as a benchmark for future algorithms on this task.
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Acknowledgments
This work was supported by the Russian Science Foundation (Project No. 18-71-00143).
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Shikunov, M., Panov, A.I. (2020). Hierarchical Reinforcement Learning Approach for the Road Intersection Task. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2019. BICA 2019. Advances in Intelligent Systems and Computing, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-25719-4_64
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DOI: https://doi.org/10.1007/978-3-030-25719-4_64
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