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Deep Reinforcement Learning for Autonomous Driving: A Survey

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arxiv 2002.00444 v2 pith:ZAXG3OPE submitted 2020-02-02 cs.LG cs.AIcs.RO

B Ravi Kiran , Ibrahim Sobh , Victor Talpaert , Patrick Mannion , Ahmad A. Al Sallab , Senthil Yogamani , Patrick Pérez This is my paper · ORCID
classification cs.LGcs.AIcs.RO
keywords learningreinforcementdeepdrivingagentsalgorithmsautonomousmethods
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With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Policy-Based Trajectory Clustering in Offline Reinforcement Learning

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Proposes Policy-Guided K-means and Centroid-Attracted Autoencoder for clustering offline RL trajectories by their generating policy, with a finite-step convergence result and an NP-completeness connection to graph coloring.

  2. Precision positioning in free-space optical communication systems via PID control tuned by RL

    physics.ins-det 2026-07 conditional novelty 5.0 of 10

    A DDPG agent tuned PID coefficients on a physical free-space-optics deflector over a 200 km UDP link, reducing radial tracking error range by 31% on a pseudo-random trajectory while leaving static-position performance...

  3. A Framework for Adversarial Analysis of Decision Support Systems Prior to Deployment

    cs.LG 2025-05 conditional novelty 4.0 of 10

    A framework for pre-deployment adversarial analysis of DRL decision-support systems, demonstrated in the CyberStrike game, ranks attack targets and shows partial attack transferability across training algorithms.

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