  Text-based games are suitable test-beds for designing agents that can learn
by interaction with the environment in the form of natural language text. Very
recently, deep reinforcement learning based agents have been successfully
applied for playing text-based games. In this paper, we explore the possibility
of designing a single agent to play several text-based games and of expanding
the agent's vocabulary using the vocabulary of agents trained for multiple
games. To this extent, we explore the application of recently proposed policy
distillation method for video games to the text-based game setting. We also use
text-based games as a test-bed to analyze and hence understand policy
distillation approach in detail.
