In experience-constrained federated RL for UAVs, learning performance depends primarily on experience reuse and minibatch size rather than the number of participating learners.
Impala: Scalable dis- tributed deep-rl with importance weighted actor-learner architectures
2 Pith papers cite this work. Polarity classification is still indexing.
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A distilled student policy using monocular depth estimation from cameras outperforms a 2D LiDAR teacher policy in navigating complex 3D obstacles while running fully onboard a Jetson Orin.
citing papers explorer
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Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments
In experience-constrained federated RL for UAVs, learning performance depends primarily on experience reuse and minibatch size rather than the number of participating learners.
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Learning Vision-Based Omnidirectional Navigation: A Teacher-Student Approach Using Monocular Depth Estimation
A distilled student policy using monocular depth estimation from cameras outperforms a 2D LiDAR teacher policy in navigating complex 3D obstacles while running fully onboard a Jetson Orin.