mlsanityData to 14 Oct 2026

Learning to Drive Safely with Hybrid Options

arXiv 2510.24674 ↗First posted 28 Oct 2025cs.LG cs.AI cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Out of the many deep reinforcement learning approaches for autonomous driving, only few make use of the options (or skills) framework. That is surprising, as this framework is naturally suited for hierarchical control applications in general, and autonomous driving tasks in specific. Therefore, in this work the options framework is applied and tailored to autonomous driving tasks on highways. More specifically, we define dedicated options for longitudinal and lateral manoeuvres with embedded safety and comfort constraints. This way, prior domain knowledge can be incorporated into the learning process and the learned driving behaviour can be constrained more easily. We propose several setups for hierarchical control with options and derive practical algorithms following state-of-the-art reinforcement learning techniques. By separately selecting actions for longitudinal and lateral control, the introduced policies over combined and hybrid options obtain the same expressiveness and flexibility that human drivers have, while being easier to interpret than classical policies over continuous actions. Of all the investigated approaches, these flexible policies over hybrid options perform the best under varying traffic conditions, outperforming the baseline policies over actions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 24 of its 41 references that are in the map. First posted on arXiv 28 Oct 2025.

Builds on

24 of 41 references.

References · 24

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2017Constrained Policy Optimizationcited by 983
  4. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  5. 2016The Option-Critic Architecturecited by 448
  6. 2020Accelerating Reinforcement Learning with Learned Skill Priorscited by 223
  7. 2020Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehiclescited by 110
  8. 2019MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policiescited by 108
  9. 2016A Deep Hierarchical Approach to Lifelong Learning in Minecraftcited by 90
  10. 2021The Option Keyboard: Combining Skills in Reinforcement Learningcited by 67
  11. 2015Reinforcement Learning with Parameterized Actionscited by 63
  12. 2018Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Spacecited by 58
12 more
  1. 2019Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Spacecited by 54
  2. 2020Continuous-Discrete Reinforcement Learning for Hybrid Control in Roboticscited by 44
  3. 2019DAC: The Double Actor-Critic Architecture for Learning Optionscited by 43
  4. 2019Driving in Dense Traffic with Model-Free Reinforcement Learningcited by 35
  5. 2019Autonomous Highway Driving using Deep Reinforcement Learningcited by 22
  6. 2021SKID RAW: Skill Discovery from Raw Trajectoriescited by 22
  7. 2019Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environmentcited by 21
  8. 2020Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learningcited by 17
  9. 2023Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skillscited by 14
  10. 2022Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skillscited by 12
  11. 2023Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approachcited by 3
  12. 2024A Dual Perspective of Reinforcement Learning for Imposing Policy Constraintscited by 1

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  3. 2021Spatially and Seamlessly Hierarchical Reinforcement Learning for State Space and Policy space in Autonomous Driving
  4. 2026Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency
  5. 2025Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning
  6. 2025Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection
  7. 2021Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving
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