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pro vyhledávání: '"Bobrin, Maksim"'
We present a new approach for Neural Optimal Transport (NOT) training procedure, capable of accurately and efficiently estimating optimal transportation plan via specific regularization on dual Kantorovich potentials. The main bottleneck of existing
Externí odkaz:
http://arxiv.org/abs/2403.03777
Offline Reinforcement Learning (RL) addresses the problem of sequential decision-making by learning optimal policy through pre-collected data, without interacting with the environment. As yet, it has remained somewhat impractical, because one rarely
Externí odkaz:
http://arxiv.org/abs/2402.13037