Non-sequential Bayesian Multi-modal Inverse Reinforcement Learning
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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2021-12-13
Department
Major/Subject
Machine Learning, Data Science and Artificial Intelligence
Mcode
SCI3044
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
31+1
Series
Abstract
When modeling agents doing multiple or complex tasks, most research has done so in two stages, clustering the subtasks and then modeling each task independently of the others. Instead we propose a solution which would model both the tasks themselves and the dynamics of how the agent switches between these simultaneously. We experiment with multiple model architectures and progressively generalize the problem based on the number of tasks and switches between them. Our method successfully models a single reward function switch, but fails with multiple changes. We discuss why this happens and future directions of research to address these problems.Description
Supervisor
Kaski, SamuelThesis advisor
Murena, Pierre-AlexandreKeywords
inverse reinforcement learning, Bayesian, Markov chain Monte Carlo, multi-task IRL