Proof of Stake (PoS) blockchains offer promising alternatives to traditional Proof of Work (PoW) systems, providing scalability and energy efficiency. However, blockchains operate in a decentralized manner and the network is composed of diverse users. This openness creates the potential for malicious nodes to disrupt the network in various ways. Therefore, it is crucial to embed a mechanism within the blockchain network to constantly monitor, identify, and eliminate these malicious nodes without involving any central authority. In this paper, we propose MRL-PoS+, a novel consensus algorithm to enhance the security of PoS blockchains by leveraging Multi-agent Reinforcement Learning (MRL) techniques. Our proposed consensus algorithm introduces a penalty-reward scheme for detecting and eliminating malicious nodes. This approach involves the detection of behaviors that can lead to potential attacks in a blockchain network and hence penalizes the malicious nodes, restricting them from performing certain actions. Our developed Proof of Concept demonstrates effectiveness in eliminating malicious nodes for six types of major attacks. Experimental results demonstrate that MRL-PoS+ significantly improves the attack resilience of PoS blockchains compared to the traditional schemes without incurring additional computation overhead.

翻译：权益证明（PoS）区块链为传统工作量证明（PoW）系统提供了具有前景的替代方案，具备可扩展性与能源效率优势。然而，区块链以去中心化方式运行，网络由多样化用户构成。这种开放性使得恶意节点可能以多种方式破坏网络。因此，在区块链网络中嵌入一种无需中央权威机构介入、能够持续监控、识别并清除恶意节点的机制至关重要。本文提出MRL-PoS+——一种通过利用多智能体强化学习（MRL）技术增强PoS区块链安全性的新型共识算法。该算法引入基于惩罚-奖励机制的方案以检测并清除恶意节点，其核心在于识别可能导致区块链网络潜在攻击的行为，进而对恶意节点实施惩罚以限制其执行特定操作。我们开发的概念验证表明，该方法能有效清除六类主要攻击中的恶意节点。实验结果表明，与传统方案相比，MRL-PoS+在未引入额外计算开销的情况下，显著提升了PoS区块链的抗攻击能力。