FinRL-Meta’s Plan for Financial Reinforcement Learning Environments
Summary
FinRL-Meta addresses a research infrastructure problem: deep reinforcement learning has potential in finance, but researchers need realistic market environments and shared benchmarks to develop and compare methods. The document contrasts this need with established reinforcement learning environments built for areas such as robotics and games, which offer few dedicated financial tasks.
The project proposes an open-ended collection of market settings and benchmarks spanning tasks such as stock and cryptocurrency trading. Its longer-term aim is to support research into agents that can simulate markets or help investigate risk and market fragility. These are stated goals rather than demonstrated results: the document gives no benchmark specifications, validation, performance evidence, or details on how realistic the proposed environments will be. Researchers should treat the described metaverse as a vision for research infrastructure, not as an evaluated trading method.
Key ideas
- Financial reinforcement learning research needs realistic environments and shared benchmarks.
- Existing general-purpose reinforcement learning libraries offer few dedicated financial tasks.
- FinRL-Meta proposes market environments spanning assets such as stocks and cryptocurrencies.
- Potential applications include market simulation, risk assessment, and research into market fragility.
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Full text
# background :github_url: https://github.com/AI4Finance-Foundation/FinRL ============================= Background ============================= Why FinRL-Meta? ================ 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.
Shown in full with attribution under the source's licence. Licence: MIT
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.