FrozenLake Environment

该数据集是对OpenAI Gym的FrozenLake环境的变体，用于评估代理获取的不同行为，以了解偏好如何随着环境波动而演变，并量化探索与偏好满足之间的权衡。该环境挑战代理在一个由冰面、陷阱和目标格组成的网格世界中导航，这为在静态和波动环境中研究偏好学习提供了可能。这项任务是在网格世界环境中进行偏好学习和探索。

This dataset is a variant of the FrozenLake environment from OpenAI Gym, designed to evaluate the diverse behaviors acquired by agents, investigate how preferences evolve alongside environmental fluctuations, and quantify the trade-off between exploration and preference satisfaction. This environment challenges agents to navigate a grid world composed of ice surfaces, traps, and goal cells, which enables research on preference learning in both static and fluctuating environments. This task focuses on preference learning and exploration within grid-world environments.