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Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using a deep neural network as its function approximator and by learning directly from raw images. A drawback of using raw images is that deep RL must learn the state feature representation from the raw images in addition to learning a policy. As a result, deep RL can require a prohibitively large amount of training time and data to reach reasonable performance, making it difficult to use deep
arXiv:1709.04083v2
fatcat:jm75zoaffncrzejzpp247fno5u