Decentralized reinforcement learning with local receptive fields can reconfigure pivoting cube ensembles into target shapes, achieving near-optimal move counts with multiple local message-passing rounds.
Deep q-learning versus proximal policy optimization: Performance comparison in a material sorting task
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Decentralised self-organisation of pivoting cube ensembles using geometric deep learning
Decentralized reinforcement learning with local receptive fields can reconfigure pivoting cube ensembles into target shapes, achieving near-optimal move counts with multiple local message-passing rounds.