  Deep reinforcement learning (deep RL) is a combination of deep learning with
reinforcement learning principles to create efficient methods that can learn by
interacting with its environment. This led to breakthroughs in many complex
tasks that were previously difficult to solve. However, deep RL requires a
large amount of training time that makes it difficult to use in various
real-life applications like human-computer interaction (HCI). Therefore, in
this paper, we study pre-training in deep RL to reduce the training time and
improve the performance in speech recognition, a popular application of HCI. We
achieve significantly improved performance in less time on a publicly available
speech command recognition dataset.
