mlsanityData to 14 Oct 2026

Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning

arXiv 2403.10761 ↗First posted 16 Mar 2024cs.AI cs.LG cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Recently there has been a growing interest in industry and academia, regarding the use of wireless chargers to prolong the operational longevity of unmanned aerial vehicles (commonly knowns as drones). In this paper we consider a charger-assisted drone application: a drone is deployed to observe a set points of interest, while a charger can move to recharge the drone's battery. We focus on the route and charging schedule of the drone and the mobile charger, to obtain high observation utility with the shortest possible time, while ensuring the drone remains operational during task execution. Essentially, this proposed drone-charger scheduling problem is a multi-stage decision-making process, in which the drone and the mobile charger act as two agents who cooperate to finish a task. The discrete-continuous hybrid action space of the two agents poses a significant challenge in our problem. To address this issue, we present a hybrid-action deep reinforcement learning framework, called HaDMC, which uses a standard policy learning algorithm to generate latent continuous actions. Motivated by representation learning, we specifically design and train an action decoder. It involves two pipelines to convert the latent continuous actions into original discrete and continuous actions, by which the drone and the charger can directly interact with environment. We embed a mutual learning scheme in model training, emphasizing the collaborative rather than individual actions. We conduct extensive numerical experiments to evaluate HaDMC and compare it with state-of-the-art deep reinforcement learning approaches. The experimental results show the effectiveness and efficiency of our solution.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 21 of its 64 references that are in the map. First posted on arXiv 16 Mar 2024.

Builds on

21 of 64 references.

References · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  4. 2015Trust Region Policy Optimizationcited by 2,580
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  6. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  7. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  8. 2015Adversarial Autoencoderscited by 364
  9. 2019Reinforcement Learning in Healthcare: A Surveycited by 310
  10. 2017Noisy Networks for Explorationcited by 266
  11. 2020Reinforcement Learning for Combinatorial Optimization: A Surveycited by 236
  12. 2018Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Spacecited by 58
9 more
  1. 2019Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Spacecited by 54
  2. 2019Discrete and Continuous Action Representation for Practical RL in Video Gamescited by 39
  3. 2021HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representationcited by 35
  4. 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 28
  5. 2019Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computingcited by 16
  6. 2022UAV Assisted Data Collection for Internet of Things: A Surveycited by 9
  7. 2021Joint Speed Control and Energy Replenishment Optimization for UAV-assisted IoT Data Collection with Deep Reinforcement Transfer Learningcited by 3
  8. 2023Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidancecited by 3
  9. 2022Mobile Wireless Rechargeable UAV Networks: Challenges and Solutionscited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning for AMR Charging Decisions: The Impact of Reward and Action Space Design
  2. 2025A Simple and Reproducible Hybrid Solver for a Truck-Drone VRP with Recharge
  3. 2025Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers
  4. 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
  5. 2026Deep Reinforcement Learning for Dynamic Battery Management of Autonomous Order Pickers
  6. 2025Coordinated Multi-Drone Last-mile Delivery: Learning Strategies for Energy-aware and Timely Operations
  7. 2025Deep Reinforcement Learning Enabled Persistent Surveillance with Energy-Aware UAV-UGV Systems for Disaster Management Applications
  8. 2026Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Reward