Online probabilistic knowledge distillation on cryptocurrency trading using Deep Reinforcement Learning

【Author】 Moustakidis, Vasileios; Passalis, Nikolaos; Tefas, Anastasios

【Source】PATTERN RECOGNITION LETTERS

【影响因子】4.757

【Abstract】Leveraging Deep Reinforcement Learning (DRL) for training agents for financial trading has gained significant attention in recent years. However, training these agents in noisy financial environments remains challenging and unstable, significantly impacting their performance as trading agents, as the recent literature has also showcased. This paper introduces a novel distillation method for DRL agents, aiming to improve the training stability of DRL agents. The proposed method transfers knowledge from a teacher ensemble to a student model, incorporating both the action probability distribution knowledge from the output layer, as well as the knowledge from the intermediate layers of the teacher's network. Furthermore, the proposed method also works in an online fashion, allowing for eliminating the separate teacher training process typically involved in many DRL distillation pipelines, simplifying the distillation process. The proposed method is extensively evaluated on a large-scale cryptocurrency trading setup, demonstrating its ability to both lead to significant improvements in trading accuracy and obtained profit, as well as increase the stability of the training process.

【Keywords】Financial Trading; Deep reinforcement learning; Online probabilistic acknowledgment; distillation

【发表时间】2024 OCT

【收录时间】2024-11-12

【文献类型】理论模型

【主题类别】

区块链应用-虚拟经济-加密货币