Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control
An online model-based reinforcement learning framework that learns a probabilistic dynamics ensemble model from scratch for sampling-based model predictive control and achieves higher sample efficiency than the evaluated model-based reinforcement learning baselines is presented.
Claudio Canales, Nan Fang, Marco Hutter et al.
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