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<p>The rapid evolution of blockchain ecosystems has highlighted the necessity for efficient interoperability across heterogeneous blockchain networks. However, the dynamic and decentralized nature of multi-chain systems introduces substantial challenges in optimizing cross-chain communication, transaction routing, and resource coordination. Conventional reinforcement learning (RL) algorithms often face scalability bottlenecks due to <i>Q</i>-value divergence and the exponential growth of state-action spaces. To address these limitations, this paper proposes a hierarchical distributed reinforcement learning (HDRL) framework that integrates hierarchical decision-making, distributed optimization, and graph neural networks (GNNs) for adaptive cross-chain coordination. In the proposed architecture, blockchain nodes act as autonomous agents performing local optimizations while collectively pursuing a global objective through a communication-efficient collaboration protocol. The framework formalizes cross-chain interactions as a set of Markov decision processes (MDPs), enabling decentralized policy learning under resource and latency constraints. Experimental results demonstrate that the HDRL approach achieves up to 750 transactions per second (TPS), reduces average latency to approximately 120&#xa0;ms, and maintains resource utilization above 85%. Furthermore, HDRL exhibits accelerated convergence within 120 training epochs, surpassing federated and transfer learning baselines in both stability and efficiency. These results confirm the potential of hierarchical distributed learning to enhance interoperability and performance in large-scale heterogeneous blockchain environments.</p>

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Distributed reinforcement learning for heterogeneous blockchain networks: a customized multi-agent architecture for cross-chain optimization

  • 期刊论文

  • 发表日期：

    2025年12月17日

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  • Sajjad Daliri,
  • Somayyeh Jafarali Jassbi

摘要

The rapid evolution of blockchain ecosystems has highlighted the necessity for efficient interoperability across heterogeneous blockchain networks. However, the dynamic and decentralized nature of multi-chain systems introduces substantial challenges in optimizing cross-chain communication, transaction routing, and resource coordination. Conventional reinforcement learning (RL) algorithms often face scalability bottlenecks due to Q-value divergence and the exponential growth of state-action spaces. To address these limitations, this paper proposes a hierarchical distributed reinforcement learning (HDRL) framework that integrates hierarchical decision-making, distributed optimization, and graph neural networks (GNNs) for adaptive cross-chain coordination. In the proposed architecture, blockchain nodes act as autonomous agents performing local optimizations while collectively pursuing a global objective through a communication-efficient collaboration protocol. The framework formalizes cross-chain interactions as a set of Markov decision processes (MDPs), enabling decentralized policy learning under resource and latency constraints. Experimental results demonstrate that the HDRL approach achieves up to 750 transactions per second (TPS), reduces average latency to approximately 120 ms, and maintains resource utilization above 85%. Furthermore, HDRL exhibits accelerated convergence within 120 training epochs, surpassing federated and transfer learning baselines in both stability and efficiency. These results confirm the potential of hierarchical distributed learning to enhance interoperability and performance in large-scale heterogeneous blockchain environments.