Finance is a particularly difficult playground for deep reinforcement learning (DRL). Some existing works already showed great potential of DRL in financial applications. However, establishing high-quality market environments and benchmarks on financial reinforcement learning are challenging and highly demanded. Thus, we proposed and started FinRL-Meta.
Envrionments and Benchmarks¶
MuJoCo and OpenAI’s XLand are famous libraries in the RL area, they built environments for deep reinforcement learning in robotics, games, and common tasks that are widely used in RL academia and industry. However, they barely provide any high quality environments for financial tasks. FinRL-Meta, previously called Neo-FinRL (near real market environments for data driven financial RL), are working to provide hundreds of market environments and tens of benchmarks for financial reinforcement learning.
Metaverse for financial RL¶
Achieving the goal of hundreds of market environments and benchmarks discribed above, we are aiming to build a metaverse for financial reinforcement learning. Like XLand, we would provide an open-ended market world with different tasks e.g. stock, cryptocurrency, etc. for agents to explore and learn.
Contribute to finance¶
We believe in the potential of deep reinforcement learning. And we hope that after we build the metaverse for financial reinforcement learning, our agents have chance to be a market simulator, or to explore risk assessment or market fragility.