arXiv cs.MA· Chih-Wei Huang, Hsin-Chieh Lee, Yun-Chen Yu, Ibrahim Althamary, Rongxing Lu, Phone Lin·· 12 小时前AI 评分18
BE-CTDE：结合异常检测与 Token 经济的区块链多智能体强化学习框架
BE-CTDE: Blockchain-Enabled Multi-Agent Reinforcement Learning with Anomaly Detection and Token Economics
AI 导读
论文提出 BE-CTDE，将区块链作为训练管理层，用行为验证器比对智能体执行轨迹与共享策略建议，并通过 slashing、burning、奖励与补偿组成的 Token 机制约束偏离行为。在多智能体路径规划实验中，即使攻击者占多数，BE-CTDE 成功率仍显著高于现有学习型路径规划器，碰撞率比仅检测无经济惩罚低最多 35%、比传统 stake slashing 低最多 43%。
正文
Abstract:Multi-agent reinforcement learning supports cooperative decision-making in Internet of Things systems, but strategic deviations can disrupt coordination and undermine confidence in the training process. This paper proposes Blockchain-Enabled Centralized Training and Decentralized Execution (BE-CTDE), which uses blockchain as an active training-management layer. A behavioral verifier compares executed trajectories with shared-policy recommendations; its flags drive a token mechanism combining slashing, burning, rewards, and compensation. Retained balances determine each agent's contribution to the global policy update, connecting auditable behavior to learning influence. We derive a one-round incentive condition that accounts for action-dependent compensation and the payoff from deviation, and a finite expected exclusion-time bound under explicit conditional detection and accounting assumptions, showing perfect detection is unnecessary. An exact finite-population capacity bound characterizes the limits of the population-relative detector. In multi-agent path finding experiments, BE-CTDE outperforms a state-of-the-art learning-based path planner with substantially higher success rates under attack, even when adversaries form the majority. It also outperforms partially defended baselines in all configurations, with collision rates up to 35% lower than under detection without economic consequences and up to 43% lower than under conventional stake slashing. Together, these results characterize how behavioral verification and token feedback can support accountable cooperative learning.
| Comments: | 12 pages, 4 figures, 6 tables. Submitted to the IEEE Internet of Things Journal |
| Subjects: | Multiagent Systems (cs.MA); Cryptography and Security (cs.CR); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2610.34121 [cs.MA] |
| (or arXiv:2610.34121v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34121 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chih-Wei Huang [view email]
[v1]
Sun, 25 Oct 2026 07:39:23 UTC (2,143 KB)
来源：arXiv cs.MA · arxiv.org