A Deep Reinforcement Learning Driving Policy for Autonomous Road Vehicles
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Abstract (arXiv)
This work regards our preliminary investigation on the problem of path planning for autonomous vehicles that move on a freeway. We approach this problem by proposing a driving policy based on Reinforcement Learning. The proposed policy makes minimal or no assumptions about the environment, since no a priori knowledge about the system dynamics is required. We compare the performance of the proposed policy against an optimal policy derived via Dynamic Programming and against manual driving simulated by SUMO traffic simulator.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2021A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learningcited by 18
- 2022On the convergence of policy gradient methods to Nash equilibria in general stochastic gamescited by 13
- 2021Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPScited by 10
- 2025A Reinforcement Learning Framework for Resource Allocation in Uplink Carrier Aggregation in the Presence of Self Interferencecited by 0
- 2025A Reinforcement Learning Framework for Resource Allocation in Uplink Carrier Aggregation in the Presence of Self Interference22 Nov 2025
- 2022On the convergence of policy gradient methods to Nash equilibria in general stochastic games17 Oct 2022
- 2021Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS14 Feb 2021
- 2021A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning17 Jan 2021