Simulation-Oversight environment

该数据集的环境包括三种状态：选择、模拟和现实，这三种状态展示了基于对不完全模拟的智能体的监督干预效果。此外，该环境支持探索，并且其奖励结构在现实和模拟中都存在可以利用的缺陷。任务则是进行带有动作修改的强化学习。

The environment of this dataset encompasses three distinct states: Selection, Simulation, and Reality, which showcase the efficacy of supervised interventions applied to agents leveraging imperfect simulations. Furthermore, this environment supports exploration, and its reward structure contains exploitable flaws in both real-world and simulated settings. The corresponding task for this dataset is reinforcement learning with action modification.