Summary of Scheduling Drone and Mobile Charger Via Hybrid-action Deep Reinforcement Learning, by Jizhe Dou and Haotian Zhang and Guodong Sun
Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning
by Jizhe Dou, Haotian Zhang, Guodong Sun
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel approach to prolonging the operational longevity of unmanned aerial vehicles (UAVs), commonly known as drones, by leveraging wireless chargers. Specifically, it tackles the drone-charger scheduling problem, where a drone is deployed to observe specific points of interest while a charger moves to recharge its battery. The authors present a hybrid-action deep reinforcement learning framework, called HaDMC, which combines policy learning with an action decoder to generate continuous and discrete actions for the drone and charger. They also incorporate a mutual learning scheme to emphasize collaboration rather than individual actions. Experimental results show that HaDMC outperforms state-of-the-art approaches in terms of effectiveness and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wireless chargers can help drones stay operational longer, which is important for tasks like observing specific points of interest. To do this, a drone and charger need to work together to find the best schedule. The researchers created a special learning framework called HaDMC that helps the drone and charger make good decisions. They tested HaDMC and found it worked better than other approaches. |
Keywords
* Artificial intelligence * Decoder * Reinforcement learning
