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MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning
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.
Neural Abstract Reasoner
Abstract reasoning and logic inference are difficult problems for neural networks, yet essential to their applicability in highly structured domains.
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