Federated Reinforcement Learning with Constraint Heterogeneity
This addresses constraint heterogeneity in federated RL for applications like LLM fine-tuning and healthcare, but it is incremental as it builds on existing policy gradient methods.
The paper tackles the problem of federated reinforcement learning with heterogeneous constraints across agents, proposing federated primal-dual methods (FedNPG and FedPPO) that enable collaborative policy learning. FedNPG achieves global convergence with an O~(1/√T) rate, and FedPPO handles complex tasks using deep neural networks.
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, we propose federated primal-dual policy optimization methods based on traditional policy gradient methods. Specifically, we introduce $N$ local Lagrange functions for agents to perform local policy updates, and these agents are then scheduled to periodically communicate on their local policies. Taking natural policy gradient (NPG) and proximal policy optimization (PPO) as policy optimization methods, we mainly focus on two instances of our algorithms, ie, {FedNPG} and {FedPPO}. We show that FedNPG achieves global convergence with an $\tilde{O}(1/\sqrt{T})$ rate, and FedPPO efficiently solves complicated learning tasks with the use of deep neural networks.