Irreversible Actions in Assistance Games with a Dynamic Goal
Reinforcement Learning (RL) agents optimize reward functions to learn desirable policies in a variety of important real-world applications such as self-driving cars and recommender systems. However, in practice, it can be very difficult to specify the correct reward function for a complex problem, i...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156753 |