mlsanityData to 7 Oct 2026

Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving

arXiv 2106.12194 ↗First posted 23 Jun 2021cs.RO cs.LGcited by 3

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

Abstract (arXiv)

To further improve the learning efficiency and performance of reinforcement learning (RL), in this paper we propose a novel uncertainty-aware model-based RL (UA-MBRL) framework, and then implement and validate it in autonomous driving under various task scenarios. First, an action-conditioned ensemble model with the ability of uncertainty assessment is established as the virtual environment model. Then, a novel uncertainty-aware model-based RL framework is developed based on the adaptive truncation approach, providing virtual interactions between the agent and environment model, and improving RL's training efficiency and performance. The developed algorithms are then implemented in end-to-end autonomous vehicle control tasks, validated and compared with state-of-the-art methods under various driving scenarios. The validation results suggest that the proposed UA-MBRL method surpasses the existing model-based and model-free RL approaches, in terms of learning efficiency and achieved performance. The results also demonstrate the good ability of the proposed method with respect to the adaptiveness and robustness, under various autonomous driving scenarios.

Key facts. Cited by 3 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 22 of its 33 references that are in the map. First posted on arXiv 23 Jun 2021.

Builds on

22 of 33 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving15 Apr

Other references · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  4. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,598
  5. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  6. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 753
  7. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
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  9. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 347
  10. 2017Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuningcited by 288
  11. 2016Unsupervised Learning for Physical Interaction through Video Predictioncited by 256
  12. 2017Stochastic Variational Video Predictioncited by 234
9 more
  1. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 224
  2. 2020Never Give Up: Learning Directed Exploration Strategiescited by 210
  3. 2018Understanding the impact of entropy on policy optimizationcited by 171
  4. 2018Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansioncited by 167
  5. 2018Model-Based Value Estimation for Efficient Model-Free Reinforcement Learningcited by 152
  6. 2016Continuous Deep Q-Learning with Model-based Accelerationcited by 149
  7. 2018Deep Reinforcement Learning for Traffic Light Control in Vehicular Networkscited by 51
  8. 2020Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understandingcited by 42
  9. 2020Bridging Imagination and Reality for Model-Based Deep Reinforcement Learningcited by 15

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2.

3 papers.

  1. 2022Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Modelcited by 44
  2. 2024Partially Observable Task and Motion Planning with Uncertainty and Risk Awarenesscited by 17
  3. 2024Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Drivingcited by 0

Similar, not cited

Close in wording, no citation either way

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  8. 2021Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning