Yutong Tian1, Minghan Gao2, Qiang Gao1,*, Xiao-Hong Peng3
Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 175-188, 2024, DOI:10.32604/iasc.2024.042762
Abstract How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market. Deep reinforcement learning, which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data, is struggling to deal with fast-changing markets due to sample inefficiency. This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning (RL) approaches in non-stationary markets for the first time. In our work, the history trading data is divided into multiple… More >
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