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Federated Reinforcement Learning with Constraint Heterogeneity
May 7, 2024, 4:42 a.m. | Hao Jin, Liangyu Zhang, Zhihua Zhang
cs.LG updates on arXiv.org arxiv.org
Abstract: We study a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. In our setting, we aim to solve a reinforcement learning problem with multiple constraints while $N$ training agents are located in $N$ different environments with limited access to the constraint signals and they are expected to collaboratively learn a policy satisfying all constraint signals. Such learning problems are prevalent in scenarios of Large Language Model (LLM) fine-tuning and healthcare applications. To solve the problem, …
abstract access agents aim arxiv constraints cs.lg environments learn multiple reinforcement reinforcement learning solve stat.ml study training type while
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