Discrete and Continuous State Environments

该数据集包含了在离散和连续状态环境中进行的实验，旨在评估GVFExplorer算法与多种基准算法的性能对比。数据集涵盖了多种设置，旨在测试GVFExplorer在学习及评估多个GVFs方面的效率，包括在离散和连续环境中对不同的和相同的累积量进行测试。这些设置涵盖了不同数量的GVFs和目标策略，其任务是通过强化学习高效地评估多个通用价值函数（GVFs）。

This dataset comprises experiments carried out in both discrete and continuous state environments, with the primary objective of comparing the performance of the GVFExplorer algorithm against multiple baseline algorithms. The dataset encompasses a diverse range of experimental settings, which are designed to assess the efficiency of GVFExplorer in learning and evaluating multiple General Value Functions (GVFs), including tests on both distinct and identical cumulants across both discrete and continuous environments. These settings cover varying quantities of GVFs and target policies, with the core task being the efficient evaluation of multiple General Value Functions (GVFs) through reinforcement learning.