City Research Online

Abstract

Multi-agent reinforcement learning in mixed-motivesettings allows for the study of complex dynamics ofagent interactions. Embodied agents in partially ob-servable environments with the ability to communicatecan share information, agree on strategies, or even lieto each other.In order to study this, we propose a sim-ple environment where we can impose varying levels ofcooperation, communication and competition as pre-requisites to reach an optimal outcome. Welcome tothe jungle.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects:	P Language and Literature Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science

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