Finite-time analysis of on-policy heterogeneous federated reinforcement learning

C Zhang, H Wang, A Mitra, J Anderson - arXiv preprint arXiv:2401.15273, 2024 - arxiv.org
Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing
the sample complexity of reinforcement learning tasks by exploiting information from …
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Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning

C Zhang, H Wang, A Mitra, J Anderson - The Twelfth International … - openreview.net
Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing
the sample complexity of reinforcement learning tasks by exploiting information from …

Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning

C Zhang, H Wang, A Mitra, J Anderson - arXiv e-prints, 2024 - ui.adsabs.harvard.edu
Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing
the sample complexity of reinforcement learning tasks by exploiting information from …