2026-09-19T08:50:04Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1571782024-10-10T03:42:24Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Karaman, Sertac
Ricard, Michael
Vincent, Caroline R.
System Design and Management Program.
2024-10-09T18:26:34Z
2024-10-09T18:26:34Z
2024-09
2024-09-20T19:32:41.571Z
https://hdl.handle.net/1721.1/157178
https://orcid.org/0009-0008-9283-8420
Technological advancements in autonomous robotics, including autonomous vehicles, have created new opportunities for innovative solutions to many everyday challenges. The impact of integrating robotic agents into real-world applications may be significantly enhanced by leveraging advancements in multi-agent autonomous systems. However, the coordination required in multi-agent systems demands complex motion planning to deconflict actions and prevent collisions of vehicles moving at increasingly high speeds. This thesis explores the application of multi-agent reinforcement learning (MARL) to autonomous robotics by teaching a central controller to navigate multiple agents across various environments without collisions. The simulated scenarios range from simple, obstacle-free environments to complex environments with obstacles configured to form narrow passageways or represent other complexities in dense urban environments. The findings demonstrate the potential of MARL to achieve high accuracy in navigating these different environments, highlighting the method's flexibility and adaptability across diverse settings and the resulting implications for applying MARL to real-world scenarios.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Multi-Agent Reinforcement Learning for Autonomous Robotics
Thesis
application/pdf
Master
Master of Science in Engineering and Management
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="67fe0106-e932-4a55-96e8-99c5488c3e19">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Multi-Agent Reinforcement Learning for Autonomous Robotics</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2024-09</PublicationDate>
<Authors>
<Author>
<DisplayName>Vincent, Caroline R.</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>https://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>Technological advancements in autonomous robotics, including autonomous vehicles, have created new opportunities for innovative solutions to many everyday challenges. The impact of integrating robotic agents into real-world applications may be significantly enhanced by leveraging advancements in multi-agent autonomous systems. However, the coordination required in multi-agent systems demands complex motion planning to deconflict actions and prevent collisions of vehicles moving at increasingly high speeds. This thesis explores the application of multi-agent reinforcement learning (MARL) to autonomous robotics by teaching a central controller to navigate multiple agents across various environments without collisions. The simulated scenarios range from simple, obstacle-free environments to complex environments with obstacles configured to form narrow passageways or represent other complexities in dense urban environments. The findings demonstrate the potential of MARL to achieve high accuracy in navigating these different environments, highlighting the method's flexibility and adaptability across diverse settings and the resulting implications for applying MARL to real-world scenarios.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>