PBFT-CG-MAPPO is a consensus-conditioned multi-agent reinforcement learning framework that embeds a Practical Byzantine Fault Tolerance (PBFT) three-phase commit protocol inside the CTDE-MAPPO training loop as an additive consensus loss with zero initialization, complemented by an entropy floor that prevents premature policy collapse. Across three cooperative environments (MPE spread, SMAClite 5m_vs_6m, VMAS UAV coverage) with five seeds each, PBFT-CG-MAPPO reduces cross-seed return variance by 1.9–47.4× compared to MAPPO and produces zero catastrophic seeds. Under Byzantine injection (f = 1), the PBFT quorum maintains stable consensus rates against random and adversarial attacks, where disabling the consensus layer causes up to 93% return degradation—demonstrating that consensus provides critical protection under active attacks while remaining non-interfering in clean environments. The experimental results yield three design principles for safe consensus conditioning in MARL: replace only dissenting actions (never overwrite consenting agents), condition via additive loss with zero initialization, and enforce an entropy floor. The framework transfers from 4-agent cooperative navigation to 12-agent permafrost monitoring without modification, scaling the f < n/3 tolerance bound automatically. The codebase includes six algorithm baselines (MAPPO, MADDPG, QMIX, CommNet, TarMAC), three Byzantine attack types, ablation studies, cross-environment evaluation, and publication-quality figure generation scripts.
Xiaohu Fan· Zenodo (CERN European Organi...· 0 citations
PBFT-CG-MAPPO is a consensus-conditioned multi-agent reinforcement learning framework that embeds a Practical Byzantine Fault Tolerance (PBFT) three-phase commit protocol inside the CTDE-MAPPO training loop as an additive consensus loss with zero initialization, complemented by an entropy floor that prevents premature policy collapse. Across three cooperative environments (MPE spread, SMAClite 5m_vs_6m, VMAS UAV coverage) with five seeds each, PBFT-CG-MAPPO reduces cross-seed return variance by 1.9–47.4× compared to MAPPO and produces zero catastrophic seeds. Under Byzantine injection (f = 1), the PBFT quorum maintains stable consensus rates against random and adversarial attacks, where disabling the consensus layer causes up to 93% return degradation—demonstrating that consensus provides critical protection under active attacks while remaining non-interfering in clean environments. The experimental results yield three design principles for safe consensus conditioning in MARL: replace only dissenting actions (never overwrite consenting agents), condition via additive loss with zero initialization, and enforce an entropy floor. The framework transfers from 4-agent cooperative navigation to 12-agent permafrost monitoring without modification, scaling the f < n/3 tolerance bound automatically. The codebase includes six algorithm baselines (MAPPO, MADDPG, QMIX, CommNet, TarMAC), three Byzantine attack types, ablation studies, cross-environment evaluation, and publication-quality figure generation scripts.
Xiaohu Fan· Zenodo (CERN European Organi...· 0 citations