Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting
作者：
Highlights：
• A novel ensembling methodology of RL agents with different training experiences.
• Validation of such ensemble in intraday stock market trading.
• Different combinations of ensemble decisions in stock markets.
• Validation in different markets and periods of trading.
• A multi-resolution feature set, which captures data prices at multiple time frames.
摘要
•A novel ensembling methodology of RL agents with different training experiences.•Validation of such ensemble in intraday stock market trading.•Different combinations of ensemble decisions in stock markets.•Validation in different markets and periods of trading.•A multi-resolution feature set, which captures data prices at multiple time frames.
论文关键词：Reinforcement learning,TD-learning,Q-learning,Financial signal processing,Neural networks for finance,Trading
论文评审过程：Received 19 November 2019, Revised 30 July 2020, Accepted 30 July 2020, Available online 9 August 2020, Version of Record 11 August 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113820
