Synthetic Personalization Tasks

该数据集包含了一个合成环境，具有连续状态空间和离散动作空间，旨在评估强化学习算法在个性化任务中的性能。此外，该数据集在维度、奖励信号的几何形态以及策略网络的架构方面提供了不同的复杂度设置。它涵盖了从低维到高维的各种规模的任务，专注于强化学习在个性化领域的应用。

This dataset contains a synthetic environment with continuous state spaces and discrete action spaces, designed to evaluate the performance of reinforcement learning algorithms on personalized tasks. Furthermore, this dataset provides varying complexity settings in terms of dimensionality, the geometric morphology of reward signals, and the architecture of policy networks. It covers tasks of various scales ranging from low-dimensional to high-dimensional, focusing on the application of reinforcement learning in the personalized domain.