Mimicking the behavior of an expert player in a Reinforcement Learning (RL) Environment to enhance the training of a novice agent from scratch is called Imitation Learning (IL). In most RL environments, the state sequences an agent encounters is a Markov Decision Process. This makes mimicking difficult as it is unlikely that a new agent may encounter similar state sequences as an expert. Prior research in IL proposes to learn a mapping between expert's states and actions, needing considerable number of state-action pairs to achieve good results. We propose an alternative to IL by appending the novice's action space with frequent action sequences of the expert. This modification improves the exploration and significantly outperforms alternatives like Dataset-Aggregation. We experiment with popular Atari games and show significant and consistent growth in the score that the new agents achieve using just a few expert action sequences.
Anshumali Shrivastava's research focuses on Large Scale Machine Learning, Scalable and Sustainable Deep Learning, Randomized Algorithms for Big-Data and Graph Mining.
