## 1 Introduction
By integrating the ZT architecture with blockchain and DRL, a robust and adaptive security framework can be developed for supply chain management [3]. [PERSON] et al. [11] explored how blockchain technology can redefine trust in supply chains, highlighting the potential of combining blockchain with ZT principles. The ZT model can help address the challenges of identity-based attacks, data breaches, and physical disruptions in supply chains. However, the implementation of ZT in supply chain management is still in its infancy, and further research is needed to develop practical and scalable solutions. In this paper, we propose a novel framework that integrates the SAC algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC algorithm is a state-of-the-art DRL method that has shown superior performance in various optimization tasks [12]. By incorporating prioritized experience replay [13], the proposed approach can efficiently learn from past experiences and adapt to dynamic demand patterns. The blockchain-based zero-trust mechanism ensures secure and transparent record-keeping, while smart contracts enable automated execution of supply chain transactions. The main contributions of this work are as follows:  
* We formulate the supply chain inventory optimization problem as a Markov Decision Process and apply the SAC algorithm with prioritized experience replay to learn adaptive policies under demand uncertainty. * The blockchain architecture with smart contracts is designed to enable secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. * We integrate the SAC-based inventory optimization model with the blockchain-based zero-trust mechanism, creating a unified framework for secure and efficient supply chain management. * We conduct experiments using real-world supply chain data to evaluate the performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The proposed SAC-rainbow framework addresses the challenges of modern supply chains by leveraging the strengths of blockchain, DRL, and ZT principles. The decentralized blockchain-based approach ensures system participant registration, authentication, and access control to system resources, thus enhancing security and trust among stakeholders. The integration of smart contracts automates supply chain transactions, reducing manual errors and improving efficiency. The SAC-based inventory optimization model learns adaptive policies to handle demand uncertainty and dynamic supply chain conditions, ensuring optimal inventory levels and minimizing costs. By operating in a ZT environment, the SAC-rainbow framework continuously verifies and validates all supply chain entities and transactions, mitigating the risk of security breaches and unauthorized access. The rest of the paper is organized as follows. Section 2 provides an overview of related work on reinforcement learning, blockchain, and zero-trust security in supply chain management. Section 3 presents the problem formulation and the proposed integrated framework. Section 4 describes the experimental setup and results. Finally, Section 5 concludes the paper and discusses future research directions.