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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APA Style

Foerster, J. (2018). Deep multi-agent reinforcement learning [PhD thesis]. University of Oxford.

MLA Style

Foerster, J. Deep Multi-Agent Reinforcement Learning. University of Oxford, 2018.

Chicago Style

Foerster, J. 2018. “Deep Multi-Agent Reinforcement Learning.” PhD thesis, University of Oxford.
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Files:: DeepMARL.pdf

(Preview, pdf, 4.8MB, Terms of use)

Authors

+ Foerster, J More by this author

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

Contributors

Department:: University of Oxford
Role:: Supervisor

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

Terms of use

Copyright holder:: Foerster, J
Copyright date:: 2018

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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