Multi-world Model in Continual Reinforcement Learning
DOI:
https://doi.org/10.1609/aaai.v38i21.30555Keywords:
Continual Reinforcement Learning, Reinforcement Learning, Foundational Model, Deep LearningAbstract
World Models are made of generative networks that can predict future states of a single environment which it was trained on. This research proposes a Multi-world Model, a foundational model built from World Models for the field of continual reinforcement learning that is trained on many different environments, enabling it to generalize state sequence predictions even for unseen settings.Downloads
Published
2024-03-24
How to Cite
Shen, K. (2024). Multi-world Model in Continual Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23757-23759. https://doi.org/10.1609/aaai.v38i21.30555
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Section
AAAI Undergraduate Consortium