Language-informed Sequential Decision-making, M.Sc. Thesis Sharif University of Technology ; Soleymani Baghshah, Mahdieh (Supervisor)
Abstract
Sample efficiency and systematic generalization are two long-standing challenges in sequential decision-making problems, especially, in reinforcement learning settings. It is hypothesized that involving natural language in conjunction with other observation modalities in decision-making environments can improve generalization due to its compositional and open-ended nature, and sample efficiency due to the concise information summarized in relatively short linguistic units. By exploiting this information and the compositional structure of the language, one can achieve an abstract and factored understanding of the environment and the task at hand. To do so, it is necessary to find the proper...
Cataloging briefLanguage-informed Sequential Decision-making, M.Sc. Thesis Sharif University of Technology ; Soleymani Baghshah, Mahdieh (Supervisor)
Abstract
Sample efficiency and systematic generalization are two long-standing challenges in sequential decision-making problems, especially, in reinforcement learning settings. It is hypothesized that involving natural language in conjunction with other observation modalities in decision-making environments can improve generalization due to its compositional and open-ended nature, and sample efficiency due to the concise information summarized in relatively short linguistic units. By exploiting this information and the compositional structure of the language, one can achieve an abstract and factored understanding of the environment and the task at hand. To do so, it is necessary to find the proper...
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