S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning

S-RL Toolbox是一个专为状态表示学习（SRL）设计的综合平台，由国立高等先进技术学院和INRIA FLOWERS团队创建。该数据集包含多种模拟环境和机器人控制任务，旨在通过提供标准化的评估数据集、指标和工具，促进SRL方法的迭代学习和评估。数据集中的图像大小为224x224像素，涵盖了从简单的2D移动机器人导航到复杂的3D机器人手臂操作等多种场景。创建过程中，数据集利用了随机策略生成，确保了数据的多样性和挑战性。该数据集主要应用于强化学习领域，特别是在解决机器人控制任务中的状态表示问题，以提高学习效率和稳定性。

S-RL Toolbox is a comprehensive platform specifically designed for State Representation Learning (SRL), developed by the National Higher School of Advanced Technologies and the INRIA FLOWERS Team. This dataset encompasses a wide range of simulated environments and robotic control tasks. Its core objective is to promote iterative learning and evaluation of SRL approaches by providing standardized evaluation datasets, metrics, and tools. The images within the dataset have a fixed resolution of 224×224 pixels, covering diverse scenarios spanning from straightforward 2D mobile robot navigation to sophisticated 3D robotic arm manipulation tasks. During the development process, the dataset was generated using stochastic policies, which ensures the diversity and challenging nature of the collected data. This dataset is predominantly utilized in the reinforcement learning domain, specifically for addressing state representation issues in robotic control tasks to improve learning efficiency and stability.