Learning Effective and Human-like Policies for Strategic, Multi-Agent Games
We consider the task of building effective but human-like policies in multi-agent decision-making problems. Imitation learning (IL) is effective at predicting human actions but may not match the strength of expert humans, while reinforcement learning (RL) and search algorithms lead to strong perform...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/144569 |