mlsanityData to 7 Oct 2026

Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Reward

arXiv 2605.24992 ↗First posted 24 May 2026cs.NI cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Multi-agent reinforcement learning (MARL) has shown wide applicability in collaborative systems such as autonomous driving and smart cities for its ability of learning through interaction. With the recent development of drone networks, researchers have also applied MARL to address the trajectory planning problems. However, the dynamic environment and the limited battery capacity are still challenging for using MARL to achieve efficient collaborative task execution. In this paper, we propose an energy-aware MARL model as an attempt to tackle these challenges, leveraging Deep Q-Networks (DQN) with individual reward functions driven by the task execution progress and the remaining battery of drones. We conduct a set of simulation studies for the proposed mode and compare it with the shared reward MARL~citeLi2022MARL to explore the impact of credit assignment in MARL. The results indicate that our proposed model can achieve at least 80% success rate regardless of the task locations and lengths. Similar to the shared reward mode, the individual reward mode can achieve a better success rate when the task density is high, and it can hit nearly a 100% success rate when task density gets close to 40%. The true advantage of our proposed model with individual reward is revealed when scaling up the environment. The comparison to the shared reward MARL shows that the our proposed model is more robust towards the change of the environment size and agent numbers. It can achieve higher success rate with fewer steps due to the clarity of the goal which improves energy efficiency even better.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 55 references that are in the map. First posted on arXiv 24 May 2026.

Builds on

22 of 55 references.

References · 22

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  4. 2015Prioritized Experience Replaycited by 1,070
  5. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 475
  6. 2019QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcited by 372
  7. 2018Mean Field Multi-Agent Reinforcement Learningcited by 202
  8. 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 180
  9. 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
  10. 2019Shapley Q-value: A Local Reward Approach to Solve Global Reward Gamescited by 71
  11. 2018CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learningcited by 31
  12. 2017A Deep Policy Inference Q-Network for Multi-Agent Systemscited by 24
10 more
  1. 2020Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computingcited by 22
  2. 2020Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environmentscited by 21
  3. 2020Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learningcited by 18
  4. 2019Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimizationcited by 12
  5. 2018A Collaborative Multi-agent Reinforcement Learning Anti-jamming Algorithm in Wireless Networkscited by 10
  6. 2018Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks with Sense-and-Send Protocolcited by 8
  7. 2020Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Rewardcited by 8
  8. 2024ROER: Regularized Optimal Experience Replaycited by 3
  9. 2021Efficient UAV Trajectory-Planning using Economic Reinforcement Learningcited by 1
  10. 2024Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networkscited by 1

Built on by

Not cited yet.