Inferring Structured World Models from Videos
Name
Kapur-shreyask-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
Size
2.04 MB
Format
Adobe PDF
Checksum (MD5)
b8e6afcac21b2ace658c5dfce1c998d6
Author(s)
Kapur, Shreyas
Advisor(s)
Tenenbaum, Joshua B.
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
Advances in reinforcement learning have allowed agents to learn a variety of board games and video games at superhuman levels. Unlike humans - which can generalize to a wide range of tasks with very little experience - these algorithms typically need vast number of experience replays to perform at the same level. In this thesis, we propose a model-based reinforcement learning approach that represents the environment using an explicit symbolic model in the form of a domain-specific language (DSL) that represents the world as a set of discrete objects with underlying latent properties that govern their dynamical interactions. We present a novel, neurally guided, on-line inference technique to recover the structured world representation from raw video observations, with the intent to be used for downstream model-based planning. We qualitatively evaluate our inference performance on classical Atari games, as well as on physics-based mobile games.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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In Copyright - Educational Use Permitted
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