AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We study federated reinforcement learning in which multiple agents interact with a common Markov decision process and communicate through a central server to collaboratively learn the optimal state-action value function. Our goal is to understand whether the sample-efficiency benefits of collaboration can be retained w...
A federated Q-learning algorithm that blends ideas from both model-based and model-free RL, along with the median-of-means device from robust statistics, which guarantees exact convergence to the optimal value function in the limit of infinite samples and enjoys near-optimal finite-time rates that benefit from collabor...
It is proved that such speedups in sample-complexity require only $\tilde O\left( 1 \right)$ communication, substantially improving upon the communication costs in prior work.
This work proposes Batched SGD, a simple variant that partitions online samples into epochs and performs a single update per epoch using a refined, low-variance gradient estimate, and provides the first high-probability guarantees for FL.
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