AdaFair-MARL enforces workload fairness as an explicit second-order cone constraint in cooperative MARL via adaptive primal-dual optimization, achieving near-perfect constraint satisfaction while preserving team performance.
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium, November 2024
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
TRIDENT is a MARL framework using Richardson-Romberg gradient correction, Lyapunov-constrained trust-region updates, and a physics-informed residual critic that claims O(1/sqrt(K)) convergence to constrained Nash equilibrium with O(sqrt(K)) violation bounds and large reductions in training violation
Mechanical conscience is proposed as a trajectory-level regulatory filter for AI policies that reduces cumulative deviation from admissible regions, with claimed theoretical properties and extension to multi-agent settings.
citing papers explorer
-
AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning
AdaFair-MARL enforces workload fairness as an explicit second-order cone constraint in cooperative MARL via adaptive primal-dual optimization, achieving near-perfect constraint satisfaction while preserving team performance.
-
TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning
TRIDENT is a MARL framework using Richardson-Romberg gradient correction, Lyapunov-constrained trust-region updates, and a physics-informed residual critic that claims O(1/sqrt(K)) convergence to constrained Nash equilibrium with O(sqrt(K)) violation bounds and large reductions in training violation
-
Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence
Mechanical conscience is proposed as a trajectory-level regulatory filter for AI policies that reduces cumulative deviation from admissible regions, with claimed theoretical properties and extension to multi-agent settings.