In experience-constrained federated RL for UAVs, learning performance depends primarily on experience reuse and minibatch size rather than the number of participating learners.
Asynchronous methods for deep rein- forcement learning
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E²DT couples a Decision Transformer with a k-Determinantal Point Process that scores trajectories on return-to-go quantiles, predictive uncertainty, and stage coverage to improve sample efficiency and policy quality in robotic manipulation.
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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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E$^2$DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation
E²DT couples a Decision Transformer with a k-Determinantal Point Process that scores trajectories on return-to-go quantiles, predictive uncertainty, and stage coverage to improve sample efficiency and policy quality in robotic manipulation.