Multi-Agent Reinforcement Learning for Swarm Planetary Exploration

Multi-Agent Reinforcement Learning for Swarm Planetary Exploration

Menor de Oñate, Adrian (TU Delft Aerospace Engineering)

van Kampen, E. (mentor)

Delft University of Technology

Aerospace Engineering

2024-05-24

Exploring planetary bodies using robot swarms can potentially increase the value of the exploration missions; enabling the execution of novel measurements and explorations previously deemed impractical or unattainable. Despite its potential, the technology readiness level of planetary swarms is not very mature. This work uses multi-agent reinforcement learning to find control policies that allow swarms to autonomously explore unknown areas in a decentralized manner, contributing towards the technology readiness of the field. A multi-agent proximal policy optimization (MAPPO) algorithm is proposed for this end, where the policy uses LIDAR perception information, and the input of the value function contains local and global environment information. The algorithm finds control policies that achieve cooperation behaviors and generalize to unseen swarm sizes and environments learning with simple, sparse reward functions. Moreover, different types of reward functions, value inputs, and environment configurations are investigated. Compared with the state-of-the-art in the field, MAPPO can learn with a larger number of agents, more complicated environments, and using sparse rewards instead of dense ones.

Multi-agent reinforcement learning
Multi-agent PPO
Planetary Exploration
Swarm intelligence

http://resolver.tudelft.nl/uuid:22d7ed2c-a5ac-476e-8cfb-7a15b06a1936

Student theses

master thesis

© 2024 Adrian Menor de Oñate