mlsanityData to 7 Oct 2026

Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving

arXiv 2501.08096 ↗First posted 14 Jan 2025cs.RO cs.AI cs.ETcited by 7

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

Reinforcement Learning (RL) has shown excellent performance in solving decision-making and control problems of autonomous driving, which is increasingly applied in diverse driving scenarios. However, driving is a multi-attribute problem, leading to challenges in achieving multi-objective compatibility for current RL methods, especially in both policy updating and policy execution. On the one hand, a single value evaluation network limits the policy updating in complex scenarios with coupled driving objectives. On the other hand, the common single-type action space structure limits driving flexibility or results in large behavior fluctuations during policy execution. To this end, we propose a Multi-objective Ensemble-Critic reinforcement learning method with Hybrid Parametrized Action for multi-objective compatible autonomous driving. Specifically, an advanced MORL architecture is constructed, in which the ensemble-critic focuses on different objectives through independent reward functions. The architecture integrates a hybrid parameterized action space structure, and the generated driving actions contain both abstract guidance that matches the hybrid road modality and concrete control commands. Additionally, an uncertainty-based exploration mechanism that supports hybrid actions is developed to learn multi-objective compatible policies more quickly. Experimental results demonstrate that, in both simulator-based and HighD dataset-based multi-lane highway scenarios, our method efficiently learns multi-objective compatible autonomous driving with respect to efficiency, action consistency, and safety.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 25 of its 68 references that are in the map. First posted on arXiv 14 Jan 2025.

Builds on

25 of 68 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving12 Apr
  2. 2023Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning28 Jun
  3. 2023Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach4 Feb
  4. 2022Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving24 Aug
  5. 2021Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving26 Sep
  6. 2021Reward (Mis)design for Autonomous Driving28 Apr
  7. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning6 Jan

Other references · 18

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  4. 2016Concrete Problems in AI Safetycited by 1,582
  5. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
  6. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
  7. 2018Count-Based Exploration with the Successor Representationcited by 112
  8. 2020Learning to be Safe: Deep RL with a Safety Criticcited by 109
  9. 2021Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domaincited by 57
  10. 2020Cautious Adaptation For Reinforcement Learning in Safety-Critical Settingscited by 42
  11. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 23
  12. 2019Autonomous Highway Driving using Deep Reinforcement Learningcited by 22
6 more
  1. 2023Accelerating Reinforcement Learning with Value-Conditional State Entropy Explorationcited by 22
  2. 2020Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimationcited by 14
  3. 2018Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic Statescited by 8
  4. 2024Discretionary Lane-Change Decision and Control via Parameterized Soft Actor-Critic for Hybrid Action Spacecited by 5
  5. 2021Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robotscited by 4
  6. 2019Learning-Driven Exploration for Reinforcement Learningcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspectivecited by 2
  2. 2026FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Drivingcited by 2
  3. 2025A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approachcited by 1
  4. 2026Video2Track: From Real-World Interaction Videos to Steerable Adversarial Closed-Track Testing for Automated Driving Systemscited by 1
  5. 2025Multi-Timescale Hierarchical Reinforcement Learning for Unified Behavior and Control of Autonomous Drivingcited by 0
  6. 2026Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Drivingcited by 0
  7. 2026Comparison-Based Ordinal Learning for Proactive Driving Risk Assessmentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
  2. 2025Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Driving
  3. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  4. 2023Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors
  5. 2026Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic
  6. 2022Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning
  7. 2025Action Space Reduction Strategies for Reinforcement Learning in Autonomous Driving
  8. 2021Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment