DeepGen: A Deep Reinforcement Learning and Genetic Algorithm-Based Approach for Coverage in Unknown Environment

Nirali Sanghvi, Rajdeep Niyogi, Ribhu Mondal

2025

Abstract

In this paper, a novel approach to optimize waypoint placement and coverage in multi-agent systems in unknown environments using a combined Genetic Algorithm and Deep Reinforcement Learning has been proposed. Effective exploration and coverage are essential in various fields, such as surveillance, environmental monitoring, and precision agriculture, where agents must cover large and often unknown environments efficiently. The proposed method uses a Genetic Algorithm to identify optimal waypoint configurations that maximize coverage while minimizing overlap among waypoints, after which a deep reinforcement learning policy refines the agents’ coverage policy to adaptively navigate and explore new areas. Simulation results demonstrate that this GA-DDQN approach significantly improves both the effectiveness of coverage and computational efficiency compared to traditional single-strategy methods. This combined framework offers a robust solution for real-world applications requiring optimized, adaptive multi-agent exploration and coverage.

Paper Citation

in Harvard Style

Sanghvi N., Niyogi R. and Mondal R. (2025). DeepGen: A Deep Reinforcement Learning and Genetic Algorithm-Based Approach for Coverage in Unknown Environment. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 564-571. DOI: 10.5220/0013259400003890

in Bibtex Style

@conference{icaart25,
author={Nirali Sanghvi and Rajdeep Niyogi and Ribhu Mondal},
title={DeepGen: A Deep Reinforcement Learning and Genetic Algorithm-Based Approach for Coverage in Unknown Environment},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={564-571},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013259400003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - DeepGen: A Deep Reinforcement Learning and Genetic Algorithm-Based Approach for Coverage in Unknown Environment
SN - 978-989-758-737-5
AU - Sanghvi N.
AU - Niyogi R.
AU - Mondal R.
PY - 2025
SP - 564
EP - 571
DO - 10.5220/0013259400003890
PB - SciTePress