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pro vyhledávání: '"Hua, Xingyuan"'
In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal environmental interaction. We find the na\"ive combination of existing offli
Externí odkaz:
http://arxiv.org/abs/2405.17477
Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental problem in this scenario is how to extract positive behaviors from noisy da
Externí odkaz:
http://arxiv.org/abs/2405.17476
Federated Reinforcement Learning (FRL) has been deemed as a promising solution for intelligent decision-making in the era of Artificial Internet of Things. However, existing FRL approaches often entail repeated interactions with the environment durin
Externí odkaz:
http://arxiv.org/abs/2405.17474
Federated Reinforcement Learning (FRL) has garnered increasing attention recently. However, due to the intrinsic spatio-temporal non-stationarity of data distributions, the current approaches typically suffer from high interaction and communication c
Externí odkaz:
http://arxiv.org/abs/2405.17471