Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis

• URL: http://arxiv.org/abs/2404.08003v5
• Date: Thu, 23 Jan 2025 23:33:53 GMT
• Title: Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
• Authors: Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi, Vaneet Aggarwal, Christopher G. Brinton,
• Abstract summary: We propose a novel asynchronous federated reinforcement learning framework termed AFedPG, which constructs a global model through collaboration among $N$ agents. We analyze the theoretical global convergence bound of AFedPG, and characterize the advantage of the proposed algorithm in terms of both the sample complexity and time complexity. We empirically verify the improved performance of AFedPG in four widely used MuJoCo environments with varying numbers of agents.
• Score: 41.75366066380951
• License:
• Abstract: To improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a global model through collaboration among $N$ agents using policy gradient (PG) updates. To address the challenge of lagged policies in asynchronous settings, we design a delay-adaptive lookahead technique \textit{specifically for FedRL} that can effectively handle heterogeneous arrival times of policy gradients. We analyze the theoretical global convergence bound of AFedPG, and characterize the advantage of the proposed algorithm in terms of both the sample complexity and time complexity. Specifically, our AFedPG method achieves $O(\frac{{\epsilon}^{-2.5}}{N})$ sample complexity for global convergence at each agent on average. Compared to the single agent setting with $O(\epsilon^{-2.5})$ sample complexity, it enjoys a linear speedup with respect to the number of agents. Moreover, compared to synchronous FedPG, AFedPG improves the time complexity from $O(\frac{t_{\max}}{N})$ to $O({\sum_{i=1}^{N} \frac{1}{t_{i}}})^{-1}$, where $t_{i}$ denotes the time consumption in each iteration at agent $i$, and $t_{\max}$ is the largest one. The latter complexity $O({\sum_{i=1}^{N} \frac{1}{t_{i}}})^{-1}$ is always smaller than the former one, and this improvement becomes significant in large-scale federated settings with heterogeneous computing powers ($t_{\max}\gg t_{\min}$). Finally, we empirically verify the improved performance of AFedPG in four widely used MuJoCo environments with varying numbers of agents. We also demonstrate the advantages of AFedPG in various computing heterogeneity scenarios.

Related papers

• Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency [52.60557300927007]
  We present a $textbfMA-OSMA$ algorithm to transfer the discrete submodular problem into a continuous optimization. We also introduce a projection-free $textbfMA-OSEA$ algorithm, which effectively utilizes the KL divergence by mixing a uniform distribution. Our algorithms significantly improve the $(frac11+c)$-approximation provided by the state-of-the-art OSG algorithm.
  arXiv  Detail & Related papers  (2025-02-07T15:57:56Z)
• Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning [4.899818550820576]
  We propose a new algorithm for multi-agent reinforcement learning. We show that this learned policy converges to the optimal policy on the order of $tildeO (1/sqrtk)$ as the number of subsampled agents increases.
  arXiv  Detail & Related papers  (2024-12-01T03:45:17Z)
• Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations [92.1840862558718]
  In practical distributed systems, workers typically not homogeneous, and can have highly varying processing times. We introduce a new parallel method Freya to handle arbitrarily slow computations. We show that Freya offers significantly improved complexity guarantees compared to all previous methods.
  arXiv  Detail & Related papers  (2024-05-24T13:33:30Z)
• Convergence Analysis of Decentralized ASGD [1.8710230264817358]
  We present a novel convergence-rate analysis for decentralized asynchronous SGD (DASGD) which does not require partial synchronization among nodes nor restrictive network topologies. Our convergence proof holds for a fixed stepsize and any nonsmooth, homogeneous, L-shaped objective function.
  arXiv  Detail & Related papers  (2023-09-07T14:50:31Z)
• Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities [12.104551746465932]
  We investigate safe multi-agent reinforcement learning, where agents seek to collectively maximize an aggregate sum of local objectives while satisfying their own safety constraints. Our algorithm converges to a first-order stationary point (FOSP) at the rate of $mathcalOleft(T-2/3right)$. In the sample-based setting, we demonstrate that, with high probability, our algorithm requires $widetildemathcalOleft(epsilon-3.5right)$ samples to achieve an $epsilon$-FOSP.
  arXiv  Detail & Related papers  (2023-05-27T20:08:35Z)
• Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies [19.779044926914704]
  We develop improved global convergence guarantees for a general class of Fisher-non-degenerate parameterized policies. In this work, we propose a Normalized Policy Gradient method with Implicit Gradient Transport (N-PG-IGT) and derive a $tildemathcalO(varepsilon-2.5)$ sample complexity of this method. We further improve this complexity to $tilde mathcalmathcalO (varepsilon-2)$ by considering a Hessian-Aided Recursive Policy Gradient
  arXiv  Detail & Related papers  (2023-02-03T13:50:23Z)
• Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning [77.22019100456595]
  We show a training algorithm for distributed computation workers with varying communication frequency. In this work, we obtain a tighter convergence rate of $mathcalO!!!(sigma2-2_avg!! . We also show that the heterogeneity term in rate is affected by the average delay within each worker.
  arXiv  Detail & Related papers  (2022-06-16T17:10:57Z)
• Online Sub-Sampling for Reinforcement Learning with General Function Approximation [111.01990889581243]
  In this paper, we establish an efficient online sub-sampling framework that measures the information gain of data points collected by an RL algorithm. For a value-based method with complexity-bounded function class, we show that the policy only needs to be updated for $proptooperatornamepolylog(K)$ times. In contrast to existing approaches that update the policy for at least $Omega(K)$ times, our approach drastically reduces the number of optimization calls in solving for a policy.
  arXiv  Detail & Related papers  (2021-06-14T07:36:25Z)
• Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms [58.57004511121862]
  This paper characterizes the convergence rate and sample complexity of AC and NAC under Markovian sampling. We show that AC and NAC attain orderwise performance improvement over PG and NPG under infinite horizon due to the incorporation of critic.
  arXiv  Detail & Related papers  (2020-04-27T17:11:06Z)

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.