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Impact of Redundancy on Resilience in Distributed Optimization and Learning

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Efficient Variance-Reduced Learning Over Multi-Agent Networks

Ali H. Sayed, Bicheng Ying, Kun Yuan

This work develops a fully decentralized variance-reduced learning algorithm for multi-agent networks where nodes store and process the data locally and are only allowed to communicate with their immediate neighbors. In the proposed algorithm, there is no ...

IEEE COMPUTER SOC2018

Decentralized exact coupled optimization

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

This work develops an exact converging algorithm for the solution of a distributed optimization problem with partially-coupled parameters across agents in a multi-agent scenario. In this formulation, while the network performance is dependent on a collecti ...

2017

Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Alexandre David Olivier Maurer, Hadrien Hendrikx

In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to \textit{interrupt} an agent in order to prevent dangerous situations from happening. Yet, as part of their learni ...

EPFL2017

Interior Point Decomposition for Multi-Agent Optimization

Colin Neil Jones, Altug Bitlislioglu, Ivan Pejcic

In this paper we present the application of the interior-point decomposition (IPD) method, which was originally formulated for stochastic programming, to optimization problems involving multiple agents that are coupled through constraints and objectives. I ...

2017

Performance limits of single-agent and multi-agent sub-gradient stochastic learning

Ali H. Sayed, Bicheng Ying

This work examines the performance of stochastic sub-gradient learning strategies, for both cases of stand-alone and networked agents, under weaker conditions than usually considered in the literature. It is shown that these conditions are automatically sa ...

IEEE2016

On the Learning Behavior of Adaptive Networks - Part II: Performance Analysis

Ali H. Sayed

Part I of this paper examined the mean-square stability and convergence of the learning process of distributed strategies over graphs. The results identified conditions on the network topology, utilities, and data in order to ensure stability; the results ...

IEEE2015

On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis

Ali H. Sayed

This paper carries out a detailed transient analysis of the learning behavior of multiagent networks, and reveals interesting results about the learning abilities of distributed strategies. Among other results, the analysis reveals how combination policies ...

2015

Diffusion adaptation over networks

Ali H. Sayed

Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with proce ...

Elsevier2014

Adaptation, Learning, and Optimization over Networks

Ali H. Sayed

This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and ...

NOW Publishers2014

Evolution of division of labor in artificial societies

Pawel Lichocki

Natural and artificial societies often divide the workload between specialized members. For example, an ant worker may preferentially perform one of many tasks such as brood rearing, foraging and nest maintenance. A robot from a rescue team may specialize ...

EPFL2013