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Found 5 papers, 0 papers with code
Scalable and Independent Learning of Nash Equilibrium Policies in $n$-Player Stochastic Games with Unknown Independent Chains
Specifically, under no assumptions on the reward functions, we show the proposed algorithm converges in polynomial time in a weaker distance (namely, the averaged Nikaido-Isoda gap) to the set of $\epsilon$-NE policies with arbitrarily high probability.
Theta-Resonance: A Single-Step Reinforcement Learning Method for Design Space Exploration
Given an environment (e. g., a simulator) for evaluating samples in a specified design space and a set of weighted evaluation metrics -- one can use Theta-Resonance, a single-step Markov Decision Process (MDP), to train an intelligent agent producing progressively more optimal samples.
The Role of Local Steps in Local SGD
Our main contribution is to characterize the convergence rate of Local SGD as a function of $\{H_i\}_{i=1}^R$ under various settings of strongly convex, convex, and nonconvex local functions, where $R$ is the total number of communication rounds.
Faster Convergence of Local SGD for Over-Parameterized Models
For general convex loss functions, we establish an error bound of $\O(1/T)$ under a mild data similarity assumption and an error bound of $\O(K/T)$ otherwise, where $K$ is the number of local steps.
Communication-efficient Decentralized Local SGD over Undirected Networks
Agents have access to $F$ through noisy gradients, and they can locally communicate with their neighbors a network.
