Language Expansion In Text-Based Games
This work addresses the challenge of generalizing agents across different text-based games, but it is incremental as it adapts an existing method to a new domain.
The authors tackled the problem of designing a single agent to play multiple text-based games and expanding its vocabulary by applying policy distillation, a method previously used for video games, to this setting.
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.