FlightMARL: A Multi-Agent Reinforcement Learning Framework for Vision-Based Control of Autonomous Quadrotors

In this paper, we present FlightMARL, an extension of the Flightmare simulator that implements a modular multi-agent reinforcement learning engine capable of supporting an array of models and algorithms. We explore representation learning of various different models in this environment. We implement recurrent agents as both discrete and continuous networks. We evaluate the learned representations of the models in the multi-agent environment. We demonstrate that agents constructed from continuous-time neural networks achieve interpretable causality in their representations.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Shubert, Ryan

Advisor dc:contributor.advisor

• Rus, Daniela

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/143256

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/143256