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Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information. (arXiv:2211.00164v1 [cs.LG])
Nov. 2, 2022, 1:11 a.m. | Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes,
cs.LG updates on arXiv.org arxiv.org
Learning to control an agent from data collected offline in a rich
pixel-based visual observation space is vital for real-world applications of
reinforcement learning (RL). A major challenge in this setting is the presence
of input information that is hard to model and irrelevant to controlling the
agent. This problem has been approached by the theoretical RL community through
the lens of exogenous information, i.e, any control-irrelevant information
contained in observations. For example, a robot navigating in busy streets
needs …
More from arxiv.org / cs.LG updates on arXiv.org
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