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Found 14 papers, 2 papers with code
Asynchronous Local Computations in Distributed Bayesian Learning
To demonstrate the efficacy of the proposed algorithm, we present simulations on a toy problem as well as on real world data sets to train ML models to perform classification tasks.
Reinforcement Learning with an Abrupt Model Change
The proposed algorithm exploits a fundamental reward-detection trade-off present in these problems and uses a quickest change detection algorithm to detect the model change.
Asynchronous Bayesian Learning over a Network
We present a practical asynchronous data fusion model for networked agents to perform distributed Bayesian learning without sharing raw data.
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions
Achieving distributed reinforcement learning (RL) for large-scale cooperative multi-agent systems (MASs) is challenging because: (i) each agent has access to only limited information; (ii) issues on convergence or computational complexity emerge due to the curse of dimensionality.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph
In this work, we study MARLs with directed coordination graphs, and propose a distributed RL algorithm where the local policy evaluations are based on local value functions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent
Recently introduced distributed zeroth-order optimization (ZOO) algorithms have shown their utility in distributed reinforcement learning (RL).
Decentralized Langevin Dynamics for Bayesian Learning
Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting.
Decomposability and Parallel Computation of Multi-Agent LQR
Conditions for decomposability, an algorithm for constructing the transformation matrix, a parallel RL algorithm, and robustness analysis when the design is applied to non-homogeneous MAS are presented.
Hierarchical Reinforcement Learning
Reinforcement Learning (RL)
Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchical Approximation
The first component optimizes the performance of each independent cluster by solving the smaller-size LQR design problem in a model-free way using an RL algorithm.
A Decentralized Approach to Bayesian Learning
Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting.
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization
In this paper, we propose and analyze SQuARM-SGD, a communication-efficient algorithm for decentralized training of large-scale machine learning models over a network.
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization
In this paper, we propose and analyze SPARQ-SGD, which is an event-triggered and compressed algorithm for decentralized training of large-scale machine learning models.
Distributed Deep Learning with Event-Triggered Communication
We develop a Distributed Event-Triggered Stochastic GRAdient Descent (DETSGRAD) algorithm for solving non-convex optimization problems typically encountered in distributed deep learning.
Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning
Numerical results also show that the proposed distributed algorithm allows the individual agents to recognize the digits even though the training data corresponding to all the digits is not locally available to each agent.
Optimization and Control Systems and Control Systems and Control
