<ul><li>Policy gradient methods in federated reinforcement learning face challenges in ensuring convergence under heterogeneous environments.</li><li>Heterogeneity can lead to optimal policies being non-deterministic or time-varying in tabular environments.</li><li>Global convergence results are proven for federated policy gradient algorithms using local updates with specific conditions.</li><li>Introduction of b-RS-FedPG method shows explicit convergence rates towards near-optimal policies, outperforming federated Q-learning empirically in heterogeneous settings.</li></ul>

On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment

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