A Deep Reinforcement Learning Driving Policy for Autonomous Road Vehicles

2019/05/22 by Konstantinos Makantasis, Makantasis, Konstantinos, Maria Kontorinaki +3 · 1 citation
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robotics (cs.RO) #Traffic control and management

paper · pdf · doi:10.48550/arxiv.1905.09046

openalex publication_date 2019/05/22 · openalex created_date 2024/04/11 · openalex updated_date 2026/07/28

Abstract

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.

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• A Reinforcement Learning Framework for Resource Allocation in Uplink Carrier Aggregation in the Presence of Self Interference

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