GitHub / fzhu0628 / Fast-FedPG---Towards-Fast-Rates-for-Federated-and-Multi-Task-Reinforcement-Learning

This work is a conference paper published at IEEE CDC 2024. The paper is dedicated to finding a policy that maximizes the average of long-term cumulative rewards across environments. Included in the repository are a brief introduction of our work, the poster for the AI Symposium at NCSU, and the slides for the CDC talk.

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Topics: heterogeneity-bias, linear-speedup, policy-gradient, reinforcement-learning

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