  Deep reinforcement learning (deep RL) has achieved superior performance in
complex sequential tasks by learning directly from image input. A deep neural
network is used as a function approximator and requires no specific state
information. However, one drawback of using only images as input is that this
approach requires a prohibitively large amount of training time and data for
the model to learn the state feature representation and approach reasonable
performance. This is not feasible in real-world applications, especially when
the data are expansive and training phase could introduce disasters that affect
human safety. In this work, we use a human demonstration approach to speed up
training for learning features and use the resulting pre-trained model to
replace the neural network in the deep RL Deep Q-Network (DQN), followed by
human interaction to further refine the model. We empirically evaluate our
approach by using only a human demonstration model and modified DQN with human
demonstration model included in the Microsoft AirSim car simulator. Our results
show that (1) pre-training with human demonstration in a supervised learning
approach is better and much faster at discovering features than DQN alone, (2)
initializing the DQN with a pre-trained model provides a significant
improvement in training time and performance even with limited human
demonstration, and (3) providing the ability for humans to supply suggestions
during DQN training can speed up the network's convergence on an optimal
policy, as well as allow it to learn more complex policies that are harder to
discover by random exploration.
