Financial portfolio optimization with online deep reinforcement learning and restricted stacked autoencoder—DeepBreath
作者：
Highlights：
• Deep reinforcement learning framework called DeepBreath for portfolio management.
• Extracting high-level features using restricted stacked autoencoder.
• A convolutional neural network is employed to enforce the policy.
• The reinforcement learning framework is trained both offline and online.
• The settlement problem of stock market transactions is solved with a blockchain.
摘要
•Deep reinforcement learning framework called DeepBreath for portfolio management.•Extracting high-level features using restricted stacked autoencoder.•A convolutional neural network is employed to enforce the policy.•The reinforcement learning framework is trained both offline and online.•The settlement problem of stock market transactions is solved with a blockchain.
论文关键词：Portfolio management,Deep reinforcement learning,Restricted stacked autoencoder,Online leaning,Settlement risk,Blockchain
论文评审过程：Received 3 October 2019, Revised 10 February 2020, Accepted 13 April 2020, Available online 18 April 2020, Version of Record 7 May 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113456
