# Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2211.05952 · HTML: https://mlsanity.com/paper/2211.05952/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (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)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control](https://mlsanity.com/paper/2201.05843/) (2022)
- [Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage](https://mlsanity.com/paper/1803.07250/) (2018)
- [Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition](https://mlsanity.com/paper/2110.09018/) (2021)
- [Multi-Agent Reinforcement Learning for Dynamic Ocean Monitoring by a Swarm of Buoys](https://mlsanity.com/paper/2012.11641/) (2020)
- [Safe Coverage of Compact Domains For Second Order Dynamical Systems](https://mlsanity.com/paper/1911.06519/) (2019)
- [Safe Coverage of Moving Domains for Vehicles with Second Order Dynamics](https://mlsanity.com/paper/2009.12211/) (2020)

## Abstract (arXiv)

Collaborative autonomous multi-agent systems covering a specified area have many potential applications, such as UAV search and rescue, forest fire fighting, and real-time high-resolution monitoring. Traditional approaches for such coverage problems involve designing a model-based control policy based on sensor data. However, designing model-based controllers is challenging, and the state-of-the-art classical control policy still exhibits a large degree of sub-optimality. In this paper, we present a reinforcement learning (RL) approach for the multi-agent efficient domain coverage problem involving agents with second-order dynamics. Our approach is based on the Multi-Agent Proximal Policy Optimization Algorithm (MAPPO). Our proposed network architecture includes the incorporation of LSTM and self-attention, which allows the trained policy to adapt to a variable number of agents. Our trained policy significantly outperforms the state-of-the-art classical control policy. We demonstrate our proposed method in a variety of simulated experiments.