Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
A survey of distributed optimization
In distributed optimization of multi-agent systems, agents cooperate to minimize a global
function which is a sum of local objective functions. Motivated by applications including …
function which is a sum of local objective functions. Motivated by applications including …
A review of cooperative multi-agent deep reinforcement learning
A Oroojlooy, D Hajinezhad - Applied Intelligence, 2023 - Springer
Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …
systems in recent years. The aim of this review article is to provide an overview of recent …
Linear convergence in federated learning: Tackling client heterogeneity and sparse gradients
We consider a standard federated learning (FL) setup where a group of clients periodically
coordinate with a central server to train a statistical model. We develop a general algorithmic …
coordinate with a central server to train a statistical model. We develop a general algorithmic …
Network topology and communication-computation tradeoffs in decentralized optimization
In decentralized optimization, nodes cooperate to minimize an overall objective function that
is the sum (or average) of per-node private objective functions. Algorithms interleave local …
is the sum (or average) of per-node private objective functions. Algorithms interleave local …
An improved analysis of gradient tracking for decentralized machine learning
We consider decentralized machine learning over a network where the training data is
distributed across $ n $ agents, each of which can compute stochastic model updates on …
distributed across $ n $ agents, each of which can compute stochastic model updates on …
Achieving geometric convergence for distributed optimization over time-varying graphs
This paper considers the problem of distributed optimization over time-varying graphs. For
the case of undirected graphs, we introduce a distributed algorithm, referred to as DIGing …
the case of undirected graphs, we introduce a distributed algorithm, referred to as DIGing …
Push–pull gradient methods for distributed optimization in networks
In this article, we focus on solving a distributed convex optimization problem in a network,
where each agent has its own convex cost function and the goal is to minimize the sum of …
where each agent has its own convex cost function and the goal is to minimize the sum of …
Distributed stochastic gradient tracking methods
In this paper, we study the problem of distributed multi-agent optimization over a network,
where each agent possesses a local cost function that is smooth and strongly convex. The …
where each agent possesses a local cost function that is smooth and strongly convex. The …
Distributed gradient methods for convex machine learning problems in networks: Distributed optimization
A Nedic - IEEE Signal Processing Magazine, 2020 - ieeexplore.ieee.org
This article provides an overview of distributed gradient methods for solving convex machine
learning problems of the form minxRn (1/m) ΣR i= 1 fi (x) in a system consisting of mm …
learning problems of the form minxRn (1/m) ΣR i= 1 fi (x) in a system consisting of mm …