BARD-MARL combines policy-graph features and Bayesian trust statistics from a BayesG substrate to detect Byzantine agents in learned-communication MARL, reporting AUC-ROC values from 0.843 to 0.982 under various attacks in SUMO traffic grids.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
A generative learning model of rational inattention is introduced for travel choice, shown to correlate with the theory and reformulated as a generalized entropy-utility multinomial logit.
citing papers explorer
-
BARD-MARL: Byzantine-Agent Detection for Learned Communication in Multi-Agent Reinforcement Learning
BARD-MARL combines policy-graph features and Bayesian trust statistics from a BayesG substrate to detect Byzantine agents in learned-communication MARL, reporting AUC-ROC values from 0.843 to 0.982 under various attacks in SUMO traffic grids.
-
Information processing constraints in travel behaviour modelling: A generative learning approach
A generative learning model of rational inattention is introduced for travel choice, shown to correlate with the theory and reformulated as a generalized entropy-utility multinomial logit.