Coordination and communication in deep multi-agent reinforcement learning

Thesis

A growing number of real-world control problems require teams of software agents to solve a joint task through cooperation. Such tasks naturally arise whenever human workers are replaced by machines, such as robot arms in manufacturing or autonomous cars in transportation. At the same time, new technologies have given rise to novel cooperative control problems that are beyond human reach, such as in package routing.

Be it for physical constraints such as partial observability, robu...

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Authors: Schroeder de Witt, CA

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: communication; coordination; deep multi-agent reinforcement learning
• Subjects: Deep Multi-Agent Reinforcement Learning; Reinforcement learning; Artificial Intelligence