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cs.LG2024
Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planning
Bradley Burega, John D. Martin, Luke Kapeluck +1
We study how a Reinforcement Learning (RL) system can remain sample-efficient when learning from an imperfect model of the environment. This is particularly challenging when the le…
cs.LG2024★ 1 cited
MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning
Rafael Rafailov, Kyle Hatch, Victor Kolev +3
We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations in the context of realistic robot tasks. Recent of…