Emergent Web Servers Exemplar Results
收藏arXiv2025-09-30 收录
下载链接：
https://github.com/JuanK120/RL_EWS/tree/master/results
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资源简介：
该数据集包含了使用深度W-学习（DWN）算法对Emergent Web Servers示范进行实验的结果，并与epsilon-greedy算法以及深度Q网络（DQN）算法的性能进行了比较。数据集包含了8次实验运行的数据框架，详细记录了在不同配置下的平均响应时间和成本。该任务旨在利用多目标强化学习对自主系统进行性能优化。
This dataset contains experimental results obtained by applying the Deep W-learning (DWN) algorithm to the Emergent Web Servers benchmark, and compares its performance against the epsilon-greedy algorithm and Deep Q-Network (DQN) algorithm. It includes data from 8 experimental runs, with detailed records of average response time and cost across different configurations. This task aims to optimize the performance of autonomous systems via multi-objective reinforcement learning.
提供机构：
Implementation repository
