Deep Coordinator uses deep unfolding to adapt ADMM-DDP penalty parameters at runtime, delivering 6.18-9.44x faster comparable-quality trajectories in car and quadrotor fleet simulations while scaling to 8x larger systems.
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms, April 2021
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6representative citing papers
Entangled QMARL agents approach the Tsirelson bound of 0.854 in CHSH while unentangled versions match classical baselines, and hybrid quantum-classical setups outperform both in CoopNav.
PACMAB is a perception-aware two-sided learning framework for multi-platform mobile crowdsensing that models the setting as a dynamic hypergame and achieves at least 41% more completed tasks than benchmarks in simulations without assuming complete information.
CAMCO enforces policy constraints on multi-agent AI at deployment time via convex projection, risk-weighted Lagrangian shaping, and bounded-convergence negotiation, yielding zero violations and 92-97% utility in tested enterprise scenarios.
The paper provides stability criteria for multi-agent systems with heterogeneous model predictive game controllers and quantifies sensitivity of equilibria to objective misspecifications.
Introduces a defeasible rule-based coaching layer that converts diagnostic failures into policy rule revisions in adaptive agent-based regulatory simulations, demonstrated on an emissions-regulation ABM.
citing papers explorer
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Deep-Unfolded Coordination
Deep Coordinator uses deep unfolding to adapt ADMM-DDP penalty parameters at runtime, delivering 6.18-9.44x faster comparable-quality trajectories in car and quadrotor fleet simulations while scaling to 8x larger systems.
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Quantum Advantage in Multi Agent Reinforcement Learning
Entangled QMARL agents approach the Tsirelson bound of 0.854 in CHSH while unentangled versions match classical baselines, and hybrid quantum-classical setups outperform both in CoopNav.
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Dynamic Hypergame for Task Assignment in Multi-platform Mobile Crowdsensing Under Incomplete Information
PACMAB is a perception-aware two-sided learning framework for multi-platform mobile crowdsensing that models the setting as a dynamic hypergame and achieves at least 41% more completed tasks than benchmarks in simulations without assuming complete information.
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Safe and Policy-Compliant Multi-Agent Orchestration for Enterprise AI
CAMCO enforces policy constraints on multi-agent AI at deployment time via convex projection, risk-weighted Lagrangian shaping, and bounded-convergence negotiation, yielding zero violations and 92-97% utility in tested enterprise scenarios.
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Stability and Sensitivity Analysis for Objective Misspecifications Among Model Predictive Game Controllers
The paper provides stability criteria for multi-agent systems with heterogeneous model predictive game controllers and quantifies sensitivity of equilibria to objective misspecifications.
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Machine-Coached Policy Revision in Adaptive Agent-Based Regulatory Simulation: A Controller-Level Contestability Layer
Introduces a defeasible rule-based coaching layer that converts diagnostic failures into policy rule revisions in adaptive agent-based regulatory simulations, demonstrated on an emissions-regulation ABM.