# Efficient UAV Trajectory-Planning using Economic Reinforcement Learning

arXiv: https://arxiv.org/abs/2103.02676 · HTML: https://mlsanity.com/paper/2103.02676/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond](https://mlsanity.com/paper/1903.05289/) (2019)
- [UAV Communications for 5G and Beyond: Recent Advances and Future Trends](https://mlsanity.com/paper/1901.06637/) (2019)
- [Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions](https://mlsanity.com/paper/2007.02382/) (2020)
- [Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles](https://mlsanity.com/paper/2007.06378/) (2020)
- [Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12461/) (2020)
- [Reinforcement Learning for a Cellular Internet of UAVs: Protocol Design, Trajectory Control, and Resource Management](https://mlsanity.com/paper/1911.08771/) (2019)
- [Swarm coordination of mini-UAVs for target search using imperfect sensors](https://mlsanity.com/paper/1901.02885/) (2019)
- [Reinforcement Learning approach for Real Time Strategy Games Battle city and S3](https://mlsanity.com/paper/1602.04936/) (2016)
- [Tutorial on UAV: A Blue Sky View on Wireless Communication](https://mlsanity.com/paper/1901.02306/) (2019)

## Built on by (most cited first, 1 of 1)

- [Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Reward](https://mlsanity.com/paper/2605.24992/) (2026)

## Abstract (arXiv)

Advances in unmanned aerial vehicle (UAV) design have opened up applications as varied as surveillance, firefighting, cellular networks, and delivery applications. Additionally, due to decreases in cost, systems employing fleets of UAVs have become popular. The uniqueness of UAVs in systems creates a novel set of trajectory or path planning and coordination problems. Environments include many more points of interest (POIs) than UAVs, with obstacles and no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. This system revolves around an economic theory, in particular an auction mechanism where UAVs trade assigned POIs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. We then translate the problem into a Partially Observable Markov decision process (POMDP), which is solved using a reinforcement learning (RL) model deployed on each agent. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size. Our proposed network and economic game architecture can effectively coordinate the swarm as an emergent phenomenon while maintaining the swarm's operation. Evaluation results prove that REPlanner efficiently outperforms conventional RL-based trajectory search.