FlightMARL: A Multi-Agent Reinforcement Learning Framework for Vision-Based Control of Autonomous Quadrotors
Name
Shubert-rshubert-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
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10.56 MB
Format
Adobe PDF
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Author(s)
Shubert, Ryan
Advisor(s)
Rus, Daniela
Date Issued
February 2022
Publisher
Massachusetts Institute of Technology
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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