Smart Contract Optimization in Business Workflows Using Deep Reinforcement Learning

Abstract

Smart contracts are self-executing digital agreements deployed on blockchain platforms that automate business processes with transparency and security. While they eliminate the need for intermediaries, their major limitation lies in their static logic, which lacks adaptability to dynamic conditions such as supply chain disruptions, market fluctuations, or contract breaches. This rigidity often leads to inefficiencies, delays, and financial losses in real-world applications. To address this challenge, we propose a hybrid framework called SmartGPO, which integrates Graph Neural Networks with Proximal Policy Optimization. The research aims to enhance the adaptability and intelligence of smart contracts by combining structural awareness and decision-making capabilities. The proposed system models the contract environment as a graph, where nodes represent entities and edges denote their interactions. GNNs generate relational embeddings, which are then used by the PPO agent to learn optimal contract execution policies through reward-driven training. SmartGPO achieves superior performance in dynamic contract workflows, with an execution success rate of 98.4 % and a decision-making accuracy of 98.1 %, outperforming traditional and standalone models. The framework also demonstrates improved gas cost reduction and faster processing time. Future enhancements include integrating multi-agent learning, real-time oracle connectivity, and legal compliance layers to further improve security, scalability, and trust. This research marks a step forward in developing intelligent, adaptive smart contracts for real-world blockchain applications.