The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environments for training financial reinforcement learning (FinRL) agents is difficult due to major factors such as the low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting. In this paper, we present an updated version of FinRL-Meta, a data-centric and openly accessible library that processes dynamic datasets from real-world markets into gym-style market environments and has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we provide hundreds of market environments through an automatic data curation pipeline. Second, we provide homegrown examples and reproduce popular research papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, we provide dozens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. The codes are available at https://github.com/AI4Finance-Foundation/FinRL-Meta

由于其动态数据集的独特特征，金融市场对于深度强化学习来说是一个特别具有挑战性的游乐场。由于金融数据信噪比低、历史数据的生存偏差、模型过拟合等主要因素，构建训练金融强化学习（FinRL）智能体的高质量市场环境十分困难。在本文中，我们提出了 FinRL-Meta 的更新版本，这是一个以数据为中心且可开放访问的库，可将现实世界市场的动态数据集处理到健身房式的市场环境中，并由 AI4Finance 社区积极维护。首先，遵循 DataOps 范式，我们通过自动数据管理管道提供数百个市场环境。其次，我们提供本土示例并复制流行的研究论文，作为用户设计新交易策略的基石。我们还将该库部署在云平台上，以便用户可以可视化自己的结果，并通过社区明智的竞争来评估相对表现。第三，我们提供了数十个 Jupyter/Python 演示，这些演示被组织成课程和文档网站，以服务快速增长的社区。代码可在 https://github.com/AI4Finance-Foundation/FinRL-Meta 获取