Hybrid CFD-MOMARL framework with PCGrad enables micro-swarm navigation in pulsatile flow, achieving progress 6.5-7.0, energy 0.63-0.65, smoothness 0.97-0.99 with emergent behaviors.
Sample-efficient multi-objective learning via generalized policy improvement prioritization,
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CIMORL framework with sampling variants (TS and MPPI) uses privileged training for decentralized multi-objective multi-robot RL, reporting 21.2% hypervolume gain over baselines in cooperative and adversarial tests.
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Micro-Swarm Locomotion Optimization in Dynamic Flow using Multi-Objective Multi-Agent Reinforcement Learning
Hybrid CFD-MOMARL framework with PCGrad enables micro-swarm navigation in pulsatile flow, achieving progress 6.5-7.0, energy 0.63-0.65, smoothness 0.97-0.99 with emergent behaviors.
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Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning
CIMORL framework with sampling variants (TS and MPPI) uses privileged training for decentralized multi-objective multi-robot RL, reporting 21.2% hypervolume gain over baselines in cooperative and adversarial tests.