Year: 2025
Type: preprint
Abstract: This paper addresses the problem of dynamic asset allocation under uncertainty, which can be formulated as a linear quadratic (LQ) control problem with multiplicative noise. To handle exploration exploitation trade offs and induce sparse control actions, we introduce Tsallis entropy as a regularization term. We develop an entropy regularized policy iteration scheme and provide theoretical guarantees for its convergence. For cases where system dynamics are unknown, we further propose a fully data driven algorithm that estimates Q functions using an instrumental variable least squares approach, allowing efficient and stable policy updates. Our framework connects entropy-regularized stochastic control with model free reinforcement learning, offering new tools for intelligent decision making in finance and automation.
Source: arXiv (Cornell University)
Authors: Zhang, Haoran, Zhang, Wenhao, Wu, Xianping
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7084165103
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