Risk-sensitive and robust model-based reinforcement learning and planning
<p>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...
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| Format: | Thesis |
| Language: | English |
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2022
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