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.

翻译：本文聚焦于基于强化学习的自动驾驶策略的超参数优化。我们详细描述了在仿真环境中训练强化学习智能体的过程。随后，我们采用基于高斯过程拟合的高效全局优化算法对强化学习中的超参数进行优化。在此优化阶段之前，应用高斯过程插值来拟合代理模型，其超参数集通过拉丁超立方采样生成。为加速评估过程，采用了并行化技术。经过超参数优化流程后，确定了一组超参数，使得整体驾驶性能得到显著提升。与现有手动调优参数以及通过拉丁超立方采样初始化过程发现的超参数相比，性能提升了4%。优化完成后，我们对所得结果进行了深入分析，并进行了敏感性分析，以评估所学自动驾驶策略的鲁棒性和泛化能力。本研究的发现推动了基于高斯过程的贝叶斯优化在强化学习自动驾驶超参数优化中的应用，为开发高效可靠的自动驾驶系统提供了有价值的见解。