Jul 2026· Journal of Artificial Intelligence and Soft Computing Research· Vol 17, pp. 5 - 18· 0 citations· 28 references
Computer Science
TL;DR
A spatiotemporal topology-aware graph reinforcement learning (STA-GRL) framework that integrates a spatiotemporal gated graph attention (STGGA) module that employs a temporal gating mechanism to dynamically prioritize tasks with rapidly decaying deadlines to address multi-agent dynamic task allocation limitations.
Abstract
Abstract Multi-agent dynamic task allocation (MADTA) for UAV swarm and autonomous systems remains a formidable challenge in highly uncertain and stochastic environments, where conventional reinforcement learning methods struggle with variable input dimensions and coordination conflicts. This paper proposes a spatiotemporal topology-aware graph reinforcement learning (STA-GRL) framework to address these limitations. By modeling the environment as a dynamic bipartite graph, the framework integrates a spatiotemporal gated graph attention (STGGA) module that employs a temporal gating mechanism to dynamically prioritize tasks with rapidly decaying deadlines. A topology-aware critic is further designed to penalize spatial conflicts among agents via an enhanced adjacency matrix. Extensive simulations demonstrate that STA-GRL significantly surpasses state-of-the-art baselines. In the primary evaluation scenario with 30 agents and an intermediate task arrival rate (λ = 0.6), STA-GRL achieves a task completion rate of 86.8% and an average response time of 18.4 seconds, while reducing the average conflict rate to just 2.1%. Moreover, ablation studies confirm the critical contribution of each architectural component, with the temporal gate improving the completion rate by 7.3% and the topology-aware critic reducing conflicts by 6.4%.
This paper proposes the Locally-decoupled and Embedding-enhanced Multi-Agent Deep Deterministic Policy Gradient (LDE-MADDPG) algorithm to address poor scalability and delayed response in drone swarm dynamic obstacle avoidance under complex cooperative environments. Such autonomous coordination capabilities are also important for distributed sensing, wireless networking, and electromagnetic information exchange in future intelligent aerial systems. The algorithm introduces three key innovations beyond standard MADDPG: a Graph Attention Network module that encodes variable-length observations into fixed-dimensional embeddings for swarm-size generalization; a dual-path critic with a global branch guiding policy updates and a local branch specializing in obstacle avoidance evaluation; and a hierarchical reward integrating multi-objective signals. Evaluated across eight static and dynamic obstacle scenarios, LDE-MADDPG achieves significantly lower collision rates (2.1%–4.2% in static scenarios and 3.8%–7.2% in dynamic scenarios) than state-of-the-art baselines and reaches a 97.5% mission completion rate in 100 random scenarios. The proposed framework demonstrates robust scalability and real-time coordination capability for dynamic environments, while providing a reliable decision-making paradigm for intelligent multi-agent systems operating in communication-intensive and electromagnetically complex application scenarios.
X. Fang, K. Chen, C. Ren et al.· Advanced Electromagnetics· 0 citations
The evolution of uncrewed aerial vehicles (UAVs) into embodied intelligent agents in the low-altitude economy is reshaping edge computing networks. However, the high mobility of UAVs induces severe topology dynamics, limiting the efficacy of traditional fully connected multiagent reinforcement learning because of dimensionality and credit assignment challenges. Furthermore, existing graph attention approaches neglect explicit communication boundary constraints, leading to mismatches between value evaluation and physical topology. To address these challenges, this paper proposes the spatial-aware graph attention multiagent twin delayed deep deterministic policy gradient (SAGA-MATD3) algorithm. By embedding a dynamic spatial masking mechanism based on the communication radius into the critic network, the proposed method enforces physical reachability constraints and attenuates extraneous noise. Simulation results demonstrate that SAGA-MATD3 significantly reduces service latency and improves fairness under an acceptable energy-consumption tradeoff, achieving a 37.1% improvement in convergence reward and enabling the self-organization of robust load-balanced mesh topologies.
Ye Wang, Jingjing Wang, Jianrui Chen et al.· IEEE Transactions on Cogniti...· 0 citations
Action Generation with Topology Awareness (AGTA), a topology-aware sequential decision-making framework in MARL that integrates inter-agent correlation modeling with topology-guided decision-order optimization, and outperforms the state-of-the-art counterparts.
Kun Hu, Shanghua Wen, Wendi Wu et al.· Mathematics· 0 citations
Despite recent advances in multi-agent path finding, achieving robust coordination in dynamic and crowded warehouse environments remains a bottleneck due to training instability and inefficient credit assignment. To address these challenges, we propose BeMapper, a novel evolutionary-augmented reinforcement learning framework that integrates a multi-agent bidirectionally-coordinated network (BicNet) with a distributed actor-critic architecture. Technically, our core novelty lies in three aspects: (1) A bidirectional feature fusion mechanism that enables agents to perceive collective spatial states beyond local observations; (2) An evolutionary-driven critic selection strategy that iteratively propagates high-performing models to accelerate convergence; (3) A multi-metric scoring system that incorporates success rate variance to penalize unstable behaviors and resolve credit assignment ambiguity. Extensive experiments demonstrate the superiority of BeMapper: it achieves a 98.66% mean success rate, outperforming state-of-the-art baselines Mapper (95.51%) and BicNet (93.78%) by 3.15%and 4.88%, respectively. Crucially, BeMapper yields a significantly higher average reward of 18.81, representing a relative improvement of 1.65 over Mapper and a substantial leap over BicNet's near-zero performance (0.04). Furthermore, in more crowded scenarios, BeMapper reduces the average travel steps to 36, being 5-9 steps shorter than competing methods, effectively enhancing operational throughput while ensuring robustness for large-scale industrial automation.
A neural scheduling framework for distributed multi-robot task allocation, consisting of a multi-decoder graph attention model (MDGAM) policy model and a critic-free group relative multi-agent policy gradient (GRMAPG) training algorithm, which improves task-completion performance over existing heuristic and learning-based methods.
Allocating limited computation among concurrent learning tasks is difficult when each task must reach a target loss before a deadline but its required training effort is unknown. Existing approaches combine online loss prediction with adaptive resource allocation, yet commonly treat computation as continuously divisible throughput. We instead study a practical setting in which tasks arrive over time and computation is provided by discrete nodes. This setting introduces both uncertain demand and constrained sequential decisions. We propose MARA, which predicts future loss trajectories with conditional flow matching and coordinates compute nodes through a cooperative multi-agent autoregressive policy. A potential-based progress reward supplies intermediate training feedback while preserving the undiscounted task-completion objective. Across in-distribution, reinforcement-learning, and vision workloads, flow matching reduces remaining-resource prediction error relative to weighted least squares. At the scheduler's training load, MARA completes 63.46% of tasks on average, 8.54 percentage points above strong baseline Learning with Adaptive Resource Allocation (LARA), and remains ahead under unseen heavier workloads.
Hanye Zhao, Muning Wen, Yong Yu et al.· 0 citations