  Finance is a particularly difficult playground for deep reinforcement
learning. However, establishing high-quality market environments and benchmarks
for financial reinforcement learning is challenging due to three major factors,
namely, low signal-to-noise ratio of financial data, survivorship bias of
historical data, and model overfitting in the backtesting stage. In this paper,
we present an openly accessible FinRL-Meta library that has been actively
maintained by the AI4Finance community. First, following a DataOps paradigm, we
will provide hundreds of market environments through an automatic pipeline that
collects dynamic datasets from real-world markets and processes them into
gym-style market environments. Second, we reproduce popular 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, FinRL-Meta
provides tens of Jupyter/Python demos organized into a curriculum and a
documentation website to serve the rapidly growing community. FinRL-Meta is
available at: https://github.com/AI4Finance-Foundation/FinRL-Meta
