# An Empirical Investigation of Representation Learning for Imitation

@article{Chen2021AnEI, title={An Empirical Investigation of Representation Learning for Imitation}, author={Cynthia Chen and Xin Chen and Sam Toyer and Cody Wild and Scott Emmons and Ian S. Fischer and Kuang-Huei Lee and Neel Alex and Steven H. Wang and Ping Luo and Stuart J. Russell and P. Abbeel and Rohin Shah}, journal={ArXiv}, year={2021}, volume={abs/2205.07886} }

Imitation learning often needs a large demonstration set in order to handle the full range of situations that an agent might ﬁnd itself in during deployment. How-ever, collecting expert demonstrations can be expensive. Recent work in vision, reinforcement learning, and NLP has shown that auxiliary representation learning objectives can reduce the need for large amounts of expensive, task-speciﬁc data. Our Empirical Investigation of Representation Learning for Imitation (EIRLI) investigates…

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