重访自然选择:利用遗传算法为复杂控制任务进化动态神经网络

IF 13.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mohamed A. Taha, Mahmoud M. Saafan, Sarah M. Ayyad
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引用次数: 0

摘要

强化学习(RL)和遗传算法(GAs)被广泛应用于决策和控制任务,但它们往往存在训练时间长和效率低的问题。本文解决了在不牺牲性能的情况下,在强化学习任务中训练神经网络的更快、更精确的方法的需求。提出的方法通过引入动态优化网络架构的机制来增强GAs,在保持准确性的同时最大限度地减少不必要的复杂性。该方法包括一种动态架构自适应技术,该技术将神经网络修剪为最紧凑和有效的配置。引入了一种混合机制来改善基本特征在网络层之间的传播,减少非线性的使用,直到必要。集成了经验回放缓冲以避免冗余的适应度评估,显著降低了计算开销。此外,一种新的方法将反向传播与气体相结合,以进一步改进监督或RL任务,将其作为一种突变方法来微调模型。实验结果表明,对于具有明确奖励的简单任务,收敛速度约为几秒,而对于更复杂的任务,收敛速度约为几分钟。训练时间减少了近70%,并且由于最小的架构,该方法提供了更快的推理速度,使其适用于移动和边缘设备。由于参数数量极低,该方法将计算量减少了90%以上,特别是在推理过程中。在训练结束时,性能指标显示出与传统方法相当的结果。该方法具有可扩展性和资源效率,在模拟环境和实际应用中都优于现有的神经网络优化技术。开发的框架在MIT许可下可在https://github.com/AhmedBoin/atgen上公开获得,为更广泛的研究社区提供了开源解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Revisiting natural selection: evolving dynamic neural networks using genetic algorithms for complex control tasks

Reinforcement learning (RL) and Genetic Algorithms (GAs) are widely used in decision-making and control tasks, but they often suffer from prolonged training times and inefficiencies. This paper addresses the need for a faster and more precise method to train neural networks in RL tasks, without sacrificing performance. The proposed approach enhances GAs by introducing mechanisms that optimize network architectures dynamically, minimizing unnecessary complexity while maintaining accuracy. The methodology includes a dynamic architecture adaptation technique that trims the neural network to its most compact and effective configuration. A Blending mechanism is introduced to improve the propagation of essential features across network layers, reducing the usage of non-linearity until necessary. An experience replay buffer is integrated to avoid redundant fitness evaluations, significantly reducing computational overhead. Additionally, a novel approach combines back-propagation with GAs for further refinement in supervised or RL tasks, using it as a mutation method to fine-tune the model. Experimental results demonstrate convergence speeds of around several seconds for simple tasks with well-defined rewards, and several minutes for more complex tasks. Training time is reduced by nearly 70%, and the approach provides faster inference speeds due to minimal architecture, making it applicable for mobile and edge devices. The method reduces computation, especially during inference, by over 90% due to the extremely low number of parameters. The performance metrics show comparable results to conventional approaches at the end of training. The proposed method is scalable and resource-efficient, outperforming existing neural network optimization techniques in both simulated environments and real-world applications. The developed framework is publicly available under the MIT license at https://github.com/AhmedBoin/atgen offering an open-source solution for the broader research community.

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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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