Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning

• URL: http://arxiv.org/abs/2407.14262v1
• Date: Fri, 19 Jul 2024 12:40:08 GMT
• Title: Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning
• Authors: Nihal Acharya Adde, Hanno Gottschalk, Andreas Ebert,
• Abstract summary: We use Efficient Global Optimization algorithm to train RL agent in a simulation environment. There is a substantial increase of 4% when compared to existing manually tuned parameters.
• Score: 1.3654846342364308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms [4.0998481751764]
  We employ two open-source Large Language Models (LLMs) to analyze the optimization logs online. We study our approach in the context of step-size adaptation for (1+1)-ES.
  arXiv  Detail & Related papers  (2024-08-05T13:20:41Z)
• AutoRL Hyperparameter Landscapes [69.15927869840918]
  Reinforcement Learning (RL) has shown to be capable of producing impressive results, but its use is limited by the impact of its hyperparameters on performance. We propose an approach to build and analyze these hyperparameter landscapes not just for one point in time but at multiple points in time throughout training. This supports the theory that hyperparameters should be dynamically adjusted during training and shows the potential for more insights on AutoRL problems that can be gained through landscape analyses.
  arXiv  Detail & Related papers  (2023-04-05T12:14:41Z)
• Optimization of Annealed Importance Sampling Hyperparameters [77.34726150561087]
  Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. We present a parameteric AIS process with flexible intermediary distributions and optimize the bridging distributions to use fewer number of steps for sampling. We assess the performance of our optimized AIS for marginal likelihood estimation of deep generative models and compare it to other estimators.
  arXiv  Detail & Related papers  (2022-09-27T07:58:25Z)
• Towards Learning Universal Hyperparameter Optimizers with Transformers [57.35920571605559]
  We introduce the OptFormer, the first text-based Transformer HPO framework that provides a universal end-to-end interface for jointly learning policy and function prediction. Our experiments demonstrate that the OptFormer can imitate at least 7 different HPO algorithms, which can be further improved via its function uncertainty estimates.
  arXiv  Detail & Related papers  (2022-05-26T12:51:32Z)
• Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning [0.0]
  In reinforcement learning (RL), the information content of data gathered by the learning agent is dependent on the setting of many hyper- parameters. In this work, a novel approach for autonomous hyper- parameter setting using Bayesian optimization is proposed. Experiments reveal promising results compared to other manual tweaking and optimization-based approaches.
  arXiv  Detail & Related papers  (2021-12-15T13:10:44Z)
• Hyper-parameter optimization based on soft actor critic and hierarchical mixture regularization [5.063728016437489]
  We model hyper- parameter optimization process as a Markov decision process, and tackle it with reinforcement learning. A novel hyper- parameter optimization method based on soft actor critic and hierarchical mixture regularization has been proposed.
  arXiv  Detail & Related papers  (2021-12-08T02:34:43Z)
• Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm [97.66038345864095]
  We propose a new hyperparameter optimization method with zeroth-order hyper-gradients (HOZOG) Specifically, we first formulate hyperparameter optimization as an A-based constrained optimization problem. Then, we use the average zeroth-order hyper-gradients to update hyper parameters.
  arXiv  Detail & Related papers  (2021-02-17T21:03:05Z)
• Online hyperparameter optimization by real-time recurrent learning [57.01871583756586]
  Our framework takes advantage of the analogy between hyperparameter optimization and parameter learning in neural networks (RNNs) It adapts a well-studied family of online learning algorithms for RNNs to tune hyperparameters and network parameters simultaneously. This procedure yields systematically better generalization performance compared to standard methods, at a fraction of wallclock time.
  arXiv  Detail & Related papers  (2021-02-15T19:36:18Z)
• Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models [0.4079265319364249]
  Current state-of-the-art methods leverage Random Forests or Gaussian processes to build a surrogate model. We propose a new surrogate model based on gradient boosting. We demonstrate empirically that the new method is able to outperform some state-of-the art techniques across a reasonable sized set of classification problems.
  arXiv  Detail & Related papers  (2021-01-06T22:07:19Z)
• Bilevel Optimization: Convergence Analysis and Enhanced Design [63.64636047748605]
  Bilevel optimization is a tool for many machine learning problems. We propose a novel stoc-efficientgradient estimator named stoc-BiO.
  arXiv  Detail & Related papers  (2020-10-15T18:09:48Z)
• Towards Automatic Bayesian Optimization: A first step involving acquisition functions [0.0]
  Bayesian optimization is the state of the art technique for the optimization of black boxes, i.e., functions where we do not have access to their analytical expression. We propose a first attempt over automatic bayesian optimization by exploring several techniques that automatically tune the acquisition function.
  arXiv  Detail & Related papers  (2020-03-21T12:22:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.