mlsanityData to 14 Oct 2026

Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning

arXiv 2407.14262 ↗First posted 19 Jul 2024cs.LG cs.AI math.OCcited by 1

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

also in 1 more
Abstract (arXiv)

This paper focuses on hyperparameter optimization for autonomous driving strategies based on Reinforcement Learning. We provide a detailed description of training the RL agent in a simulation environment. Subsequently, we employ Efficient Global Optimization algorithm that uses Gaussian Process fitting for hyperparameter optimization in RL. Before this optimization phase, Gaussian process interpolation is applied to fit the surrogate model, for which the hyperparameter set is generated using Latin hypercube sampling. To accelerate the evaluation, parallelization techniques are employed. Following the hyperparameter optimization procedure, a set of hyperparameters is identified, resulting in a noteworthy enhancement in overall driving performance. There is a substantial increase of 4% when compared to existing manually tuned parameters and the hyperparameters discovered during the initialization process using Latin hypercube sampling. After the optimization, we analyze the obtained results thoroughly and conduct a sensitivity analysis to assess the robustness and generalization capabilities of the learned autonomous driving strategies. The findings from this study contribute to the advancement of Gaussian process based Bayesian optimization to optimize the hyperparameters for autonomous driving in RL, providing valuable insights for the development of efficient and reliable autonomous driving systems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 8 of its 30 references that are in the map. First posted on arXiv 19 Jul 2024.

Builds on

8 of 30 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Deep Reinforcement Learning that Matterscited by 754
  3. 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 230
  4. 2018Neural Architecture Search with Bayesian Optimisation and Optimal Transportcited by 195
  5. 2017Multi-fidelity Bayesian Optimisation with Continuous Approximationscited by 121
  6. 2017Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environmentscited by 108
  7. 2020HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisationcited by 83
  8. 2016The Parallel Knowledge Gradient Method for Batch Bayesian Optimizationcited by 74

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)cited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning
  2. 2024A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
  3. 2024Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed
  4. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
  5. 2023Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors
  6. 2025Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving
  7. 2020Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
  8. 2025Hyperparameter Optimisation with Practical Interpretability and Explanation Methods in Probabilistic Curriculum Learning