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Neural language models learn word representations, or embeddings, that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neu-ral language model. We show that embeddings from translation models outper-form those learned by monolingual models at(More)
We address the problem of adapting robotic perception from simulated to real-world environments. For many robotic control tasks, real training imagery is expensive to obtain, but a large amount of synthetic data is easy to generate through simulation. We propose a method that adapts representations using a small number of paired synthetic and real views of(More)
Neural language models learn word representations that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models. We show that translation-based embeddings outper-form those learned by cutting-edge monolingual models at single-language tasks requiring knowledge of conceptual(More)
— Reinforcement learning (RL) can automate a wide variety of robotic skills, but learning each new skill requires considerable real-world data collection and manual representation engineering to design policy classes or features. Using deep reinforcement learning to train general purpose neural network policies alleviates some of the burden of manual(More)
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