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Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
We consider the problem of fully decentralized multi-agent reinforcement...
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Decentralized Bayesian Learning over Graphs
We propose a decentralized learning algorithm over a general social netw...
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Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph
We study the decentralized machine learning scenario where many users co...
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Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach
Learning cooperative policies for multi-agent systems is often challenge...
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COKE: Communication-Censored Kernel Learning for Decentralized Non-parametric Learning
This paper studies the decentralized optimization and learning problem w...
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Self-triggered distributed k-order coverage control
A k-order coverage control problem is studied where a network of agents ...
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On the Optimal Interaction Range for Multi-Agent Systems Under Adversarial Attack
Consider a consensus-driven multi-agent dynamic system. The interaction ...
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BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization
This article deals with the problem of distributed machine learning, in which agents update their models based on their local datasets, and aggregate the updated models collaboratively and in a fully decentralized manner. In this paper, we tackle the problem of information heterogeneity arising in multi-agent networks where the placement of informative agents plays a crucial role in the learning dynamics. Specifically, we propose BayGo, a novel fully decentralized joint Bayesian learning and graph optimization framework with proven fast convergence over a sparse graph. Under our framework, agents are able to learn and communicate with the most informative agent to their own learning. Unlike prior works, our framework assumes no prior knowledge of the data distribution across agents nor does it assume any knowledge of the true parameter of the system. The proposed alternating minimization based framework ensures global connectivity in a fully decentralized way while minimizing the number of communication links. We theoretically show that by optimizing the proposed objective function, the estimation error of the posterior probability distribution decreases exponentially at each iteration. Via extensive simulations, we show that our framework achieves faster convergence and higher accuracy compared to fully-connected and star topology graphs.
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