83 citations · 104 across the 6 of their papers we have counts for
6 papers
Dynamic Datasets and Market Environments for Financial Reinforcement Learning
Xiao-Yang Liu, Ziyi Xia, Hongyang Yang +6
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environmen…
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
Xiao-Yang Liu, Ziyi Xia, Jingyang Rui +6
Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement le…
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting
Berend Jelmer Dirk Gort, Xiao-Yang Liu, Xinghang Sun +3
Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and opt…
Graph Neural Networks in IoT: A Survey
Guimin Dong, Mingyue Tang, Zhiyuan Wang +7
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the…
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance
Xiao-Yang Liu, Jingyang Rui, Jiechao Gao +5
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 ma…
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance
Xiao-Yang Liu, Hongyang Yang, Jiechao Gao +1
Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to ob…