  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 RL in real-world applications,
especially when data is expensive. In this work, we speed up training by
addressing half of what deep RL is trying to solve --- learning features. Our
approach is to learn some of the important features by pre-training deep RL
network's hidden layers via supervised learning using a small set of human
demonstrations. We empirically evaluate our approach using deep Q-network (DQN)
and asynchronous advantage actor-critic (A3C) algorithms on the Atari 2600
games of Pong, Freeway, and Beamrider. Our results show that: 1) pre-training
with human demonstrations in a supervised learning manner is better at
discovering features relative to pre-training naively in DQN, and 2)
initializing a deep RL network with a pre-trained model provides a significant
improvement in training time even when pre-training from a small number of
human demonstrations.
