# Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication

arXiv: https://arxiv.org/abs/2401.17880 · HTML: https://mlsanity.com/paper/2401.17880/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 20 of its 64 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?](https://mlsanity.com/paper/2011.09533/) (2020)
- [Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds](https://mlsanity.com/paper/1901.00210/) (2019)
- [Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2109.11251/) (2021)
- [Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs](https://mlsanity.com/paper/1905.03814/) (2019)
- [Celebrating Diversity in Shared Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.02195/) (2021)
- [Policy Certificates: Towards Accountable Reinforcement Learning](https://mlsanity.com/paper/1811.03056/) (2018)
- [Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning](https://mlsanity.com/paper/2107.01264/) (2021)
- [Multi-Agent Actor-Critic with Hierarchical Graph Attention Network](https://mlsanity.com/paper/1909.12557/) (2019)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [Offloading Optimization in Edge Computing for Deep Learning Enabled Target Tracking by Internet-of-UAVs](https://mlsanity.com/paper/2008.08001/) (2020)
- [Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs](https://mlsanity.com/paper/2202.06046/) (2022)
- [UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization](https://mlsanity.com/paper/2008.00218/) (2020)
- [Joint Power and 3D Trajectory Optimization for UAV-enabled Wireless Powered Communication Networks with Obstacles](https://mlsanity.com/paper/2310.00384/) (2023)
- [Resource Scheduling for UAVs-aided D2D Networks: A Multi-objective Optimization Approach](https://mlsanity.com/paper/2311.16116/) (2023)
- [Common Throughput Maximization in UAV-Enabled OFDMA Systems with Delay Consideration](https://mlsanity.com/paper/1801.00444/) (2018)
- [Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2112.10630/) (2021)

## Built on by (most cited first, 2 of 2)

- [The Operable Pareto Front: Distilling Offline Search into Run-Time Control for Multi-Objective UAV Edge-Computing Scheduling](https://mlsanity.com/paper/2609.17992/) (2026)
- [DSWM: Decomposed Spatio-Temporal World Model for Demand-Driven UAV Base Station Repositioning](https://mlsanity.com/paper/2609.36845/) (2026)

## Abstract (arXiv)

In the multiple unmanned aerial vehicle (UAV)- assisted downlink communication, it is challenging for UAV base stations (UAV BSs) to realize trajectory design and resource assignment in unknown environments. The cooperation and competition between UAV BSs in the communication network leads to a Markov game problem. Multi-agent reinforcement learning is a significant solution for the above decision-making. However, there are still many common issues, such as the instability of the system and low utilization of historical data, that limit its application. In this paper, a novel graph-attention multi-agent trust region (GA-MATR) reinforcement learning framework is proposed to solve the multi-UAV assisted communication problem. Graph recurrent network is introduced to process and analyze complex topology of the communication network, so as to extract useful information and patterns from observational information. The attention mechanism provides additional weighting for conveyed information, so that the critic network can accurately evaluate the value of behavior for UAV BSs. This provides more reliable feedback signals and helps the actor network update the strategy more effectively. Ablation simulations indicate that the proposed approach attains improved convergence over the baselines. UAV BSs learn the optimal communication strategies to achieve their maximum cumulative rewards. Additionally, multi-agent trust region method with monotonic convergence provides an estimated Nash equilibrium for the multi-UAV assisted communication Markov game.