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Found 8 papers, 4 papers with code
Attacking c-MARL More Effectively: A Data Driven Approach
In recent years, a proliferation of methods were developed for cooperative multi-agent reinforcement learning (c-MARL).
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
These new algorithms can handle statistical and system heterogeneity, which are the two main challenges in federated learning, while achieving the best known communication complexity.
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness
Motivated by broad applications in reinforcement learning and machine learning, this paper considers the popular stochastic gradient descent (SGD) when the gradients of the underlying objective function are sampled from Markov processes.
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
We propose a novel hybrid stochastic policy gradient estimator by combining an unbiased policy gradient estimator, the REINFORCE estimator, with another biased one, an adapted SARAH estimator for policy optimization.
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
In the expectation case, we establish $\mathcal{O}(\varepsilon^{-2})$ iteration-complexity to achieve a stationary point in expectation and estimate the total number of stochastic oracle calls for both function value and its Jacobian, where $\varepsilon$ is a desired accuracy.
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
We introduce a new approach to develop stochastic optimization algorithms for a class of stochastic composite and possibly nonconvex optimization problems.
Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization
We introduce a hybrid stochastic estimator to design stochastic gradient algorithms for solving stochastic optimization problems.
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
We also specify the algorithm to the non-composite case that covers existing state-of-the-arts in terms of complexity bounds.
