A Transformer-Based Reinforcement Learning Framework for Sequential Strategy Optimization in Sparse Data

A Transformer-Based Reinforcement Learning Framework for Sequential Strategy Optimization in Sparse Data

A deep reinforcement learning framework is presented for strategy generation and profit forecasting based on large-scale economic behavior data. By integrating perturbation-based augmentation, backward return estimation, and policy-stabilization mechanisms, the framework facilitates robust modeling...

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Bibliographic Details
Main Authors: Zizhe Zhou, Liman Zhang, Xuran Liu, Siyang He, Jingxuan Zhang, Jinzhi Zhu, Yuanping Pang, Chunli Lv
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Applied Sciences
Subjects:
deep reinforcement learning
transformer-based neural architectures
stability-constrained deep learning
sequential decision-making networks
Online Access:https://www.mdpi.com/2076-3417/15/11/6215
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