Risk-sensitive and robust model-based reinforcement learning and planning

Thesis

Many sequential decision-making problems that are currently automated, such as those in manufacturing or recommender systems, operate in an environment where there is either little uncertainty, or zero risk of catastrophe. As companies and researchers attempt to deploy autonomous systems in less constrained environments, it is increasingly important that we endow sequential decision-making algorithms with the ability to reason about uncertainty and risk.

In this thesis, we will a...

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Authors: Rigter, M

• DOI: 10.5287/ora-jnzpg6wjd
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: planning; risk; Markov decision processes; model-based; decision-making under uncertainty; robustness; reinforcement learning
• Subjects: Machine learning; Artificial intelligence; Robotics; Operations research