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Thesis

Deep multi-agent reinforcement learning

Abstract:

A plethora of real world problems, such as the control of autonomous vehicles and drones, packet delivery, and many others consists of a number of agents that need to take actions based on local observations and can thus be formulated in the multi-agent reinforcement learning (MARL) setting. Furthermore, as more machine learning systems are deployed in the real world, they will start having impact on each other, effectively turning most decision making problems into multiagent proble...

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Division:
MPLS
Department:
Computer Science
Department:
University of Oxford
Role:
Author

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Department:
University of Oxford
Role:
Supervisor

DOI:
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

UUID:
uuid:a55621b3-53c0-4e1b-ad1c-92438b57ffa4
Deposit date:
2019-09-16
ARK identifier:

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