# FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance

arXiv: https://arxiv.org/abs/2112.06753 · HTML: https://mlsanity.com/paper/2112.06753/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Open-Ended Learning Leads to Generally Capable Agents](https://mlsanity.com/paper/2107.12808/) (2021)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance](https://mlsanity.com/paper/2111.09395/) (2021)
- [Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review](https://mlsanity.com/paper/2106.00123/) (2021)
- [Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis](https://mlsanity.com/paper/1906.11046/) (2019)
- [FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance](https://mlsanity.com/paper/2111.05188/) (2021)
- [ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning](https://mlsanity.com/paper/2112.05923/) (2021)

## Built on by (most cited first, 7 of 7)

- [A Review of Reinforcement Learning in Financial Applications](https://mlsanity.com/paper/2411.12746/) (2024)
- [Reinforcement Learning in Practice: Opportunities and Challenges](https://mlsanity.com/paper/2202.11296/) (2022)
- [Language Model Guided Reinforcement Learning in Quantitative Trading](https://mlsanity.com/paper/2508.02366/) (2025)
- [The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence](https://mlsanity.com/paper/2605.16895/) (2026)
- [Orchestration Framework for Financial Agents: From Algorithmic Trading to Agentic Trading](https://mlsanity.com/paper/2512.02227/) (2025)
- [Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding](https://mlsanity.com/paper/2602.00781/) (2026)
- [Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets](https://mlsanity.com/paper/2605.17307/) (2026)

## Abstract (arXiv)

Deep reinforcement learning (DRL) has shown huge potentials in building financial market simulators recently. However, due to the highly complex and dynamic nature of real-world markets, raw historical financial data often involve large noise and may not reflect the future of markets, degrading the fidelity of DRL-based market simulators. Moreover, the accuracy of DRL-based market simulators heavily relies on numerous and diverse DRL agents, which increases demand for a universe of market environments and imposes a challenge on simulation speed. In this paper, we present a FinRL-Meta framework that builds a universe of market environments for data-driven financial reinforcement learning. First, FinRL-Meta separates financial data processing from the design pipeline of DRL-based strategy and provides open-source data engineering tools for financial big data. Second, FinRL-Meta provides hundreds of market environments for various trading tasks. Third, FinRL-Meta enables multiprocessing simulation and training by exploiting thousands of GPU cores. Our codes are available online at https://github.com/AI4Finance-Foundation/FinRL-Meta.