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.
A Review of Cooperative Multi-Agent Deep Reinforcement Learning, April 2021
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Energy-aware MARL with individual rewards for drone networks shows better robustness to larger environments and more agents than shared-reward baselines in simulations, reaching at least 80% success rate.
citing papers explorer
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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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Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Reward
Energy-aware MARL with individual rewards for drone networks shows better robustness to larger environments and more agents than shared-reward baselines in simulations, reaching at least 80% success rate.