A Communication-Efficient Decentralized Actor-Critic Algorithm

• URL: http://arxiv.org/abs/2510.19199v1
• Date: Wed, 22 Oct 2025 03:15:52 GMT
• Title: A Communication-Efficient Decentralized Actor-Critic Algorithm
• Authors: Xiaoxing Ren, Nicola Bastianello, Thomas Parisini, Andreas A. Malikopoulos,
• Abstract summary: We develop a decentralized actor-critic learning framework in which each agent performs several local updates of its policy and value function.<n>This local training strategy substantially reduces the communication burden while maintaining coordination across the network.
• Score: 5.138643040049347
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we study the problem of reinforcement learning in multi-agent systems where communication among agents is limited. We develop a decentralized actor-critic learning framework in which each agent performs several local updates of its policy and value function, where the latter is approximated by a multi-layer neural network, before exchanging information with its neighbors. This local training strategy substantially reduces the communication burden while maintaining coordination across the network. We establish finite-time convergence analysis for the algorithm under Markov-sampling. Specifically, to attain the $\varepsilon$-accurate stationary point, the sample complexity is of order $\mathcal{O}(\varepsilon^{-3})$ and the communication complexity is of order $\mathcal{O}(\varepsilon^{-1}\tau^{-1})$, where tau denotes the number of local training steps. We also show how the final error bound depends on the neural network's approximation quality. Numerical experiments in a cooperative control setting illustrate and validate the theoretical findings.

Related papers

• Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum [78.27945336558987]
  Decentralized server (DFL) eliminates reliance on client-client architecture.<n>Non-smooth regularization is often incorporated into machine learning tasks.<n>We propose a novel novel DNCFL algorithm to solve these problems.
  arXiv  Detail & Related papers  (2025-04-17T08:32:25Z)
• Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks [86.99017195607077]
  We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a wireless network with statistically-identical agents.<n>Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies.
  arXiv  Detail & Related papers  (2024-04-04T06:24:11Z)
• Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation [77.09836892653176]
  We study multi-agent reinforcement learning in the setting of episodic Markov decision processes. We propose a provably efficient algorithm based on value that enable asynchronous communication. We show that a minimal $Omega(dM)$ communication complexity is required to improve the performance through collaboration.
  arXiv  Detail & Related papers  (2023-05-10T20:29:29Z)
• INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks [24.02913189682224]
  Decentralized bilevel optimization problems have received increasing attention in the networking machine learning communities. Low sample and communication complexities are two fundamental challenges that remain under-explored. Our work is the first that both low sample and communication complexities for solving decentralized bilevel optimization problems over networks.
  arXiv  Detail & Related papers  (2022-07-27T04:19:28Z)
• On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network [55.56019538079826]
  Bilevel optimization has been applied to a wide variety of machine learning models. Most existing algorithms restrict their single-machine setting so that they are incapable of handling distributed data. We develop novel decentralized bilevel optimization algorithms based on a gradient tracking communication mechanism and two different gradients.
  arXiv  Detail & Related papers  (2022-06-30T05:29:52Z)
• Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks [42.76623191830371]
  We propose a gossip-based distributed bilevel optimization algorithm. Agents can solve both networked and outer problems in a single time. Our algorithm achieves the state-of-the-art efficiency and test accuracy.
  arXiv  Detail & Related papers  (2022-06-22T06:38:54Z)
• Acceleration in Distributed Optimization Under Similarity [72.54787082152278]
  We study distributed (strongly convex) optimization problems over a network of agents, with no centralized nodes. An $varepsilon$-solution is achieved in $tildemathcalrhoObig(sqrtfracbeta/mu (1-)log1/varepsilonbig)$ number of communications steps. This rate matches (up to poly-log factors) for the first time lower complexity communication bounds of distributed gossip-algorithms applied to the class of problems of interest.
  arXiv  Detail & Related papers  (2021-10-24T04:03:00Z)
• Decentralized Local Stochastic Extra-Gradient for Variational Inequalities [125.62877849447729]
  We consider distributed variational inequalities (VIs) on domains with the problem data that is heterogeneous (non-IID) and distributed across many devices. We make a very general assumption on the computational network that covers the settings of fully decentralized calculations. We theoretically analyze its convergence rate in the strongly-monotone, monotone, and non-monotone settings.
  arXiv  Detail & Related papers  (2021-06-15T17:45:51Z)
• Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity [13.100926925535578]
  We develop two decentralized TD with correction (TDC) algorithms for multi-agent off-policy TD learning. Our algorithms preserve full privacy of the actions, policies and rewards of the agents, and adopt mini-batch sampling to reduce the sampling variance and communication frequency.
  arXiv  Detail & Related papers  (2021-03-24T12:48:08Z)
• Communication-efficient Decentralized Local SGD over Undirected Networks [2.3572498744567123]
  We consider the distributed learning problem where a network of $n$ agents seeks to minimize a global function $F$. We analyze the trade-off between the number of communication rounds and the computational effort of each agent. Our results show that by using only $R=Omega(n)$ communication rounds, one can achieve an error that scales as $O(1/nT)$.
  arXiv  Detail & Related papers  (2020-11-06T09:34:00Z)
• A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning [49.15799302636519]
  We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers) In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns. We show that it is possible to achieve equivalent learning performance as if the data is available in a single place.
  arXiv  Detail & Related papers  (2020-09-29T13:08:12Z)
• Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks [50.42141893913188]
  We study a distributed variable for large-scale AUC for a neural network as with a deep neural network. Our model requires a much less number of communication rounds and still a number of communication rounds in theory. Our experiments on several datasets show the effectiveness of our theory and also confirm our theory.
  arXiv  Detail & Related papers  (2020-05-05T18:08:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.