Reinforcement Learning has applications in field of mechatronics, robotics, and other resource-constrained control system. Problem of resource allocation is primarily solved using traditional predefined techniques and modern deep learning methods. The drawback of predefined and most deep learning methods for resource allocation is failing to meet the requirements in cases of uncertain system environment. We can approach problem of resource allocation in uncertain system environment alongside following certain criteria using deep reinforcement learning. Also, reinforcement learning has ability for adapting to new uncertain environment for prolonged period of time. The paper provides a detailed comparative analysis on various deep reinforcement learning methods by applying different components to modify architecture of reinforcement learning with use of noisy layers, prioritized replay, bagging, duelling networks, and other related combination to obtain improvement in terms of performance and reduction of computational cost. The paper identifies problem of resource allocation in uncertain environment could be effectively solved using Noisy Bagging duelling double deep Q network achieving efficiency of 97.7% by maximizing reward with significant exploration in given simulated environment for resource allocation.
翻译:强化学习在机械学、机器人学和其他受资源限制的控制系统等领域都有应用。资源分配问题主要通过传统预先定义的技术和现代深层学习方法来解决。在系统环境不确定的情况下,预先确定和最深层次的资源分配方法的缺点无法满足要求。我们可以在采用深度强化学习的某些标准的同时,在不确定的系统环境中解决资源分配问题。强化学习能够长期适应新的不确定环境。文件对各种深层强化学习方法进行了详细的比较分析,采用不同的组成部分来修改强化学习结构,利用噪音层、优先重播、包装、拆分网络和其他相关组合来改进绩效和减少计算成本。文件指出,在不确定环境中的资源分配问题可以通过Noisy Blagging 双层深层Q网络有效解决。通过在模拟环境进行重大探索以获得最大回报,实现97.7%的效率。