Building stronger semantic understanding into text game reinforcement learning agents
AI agents capable of understanding natural language, communicating, and accomplishing tasks hold the promise of revolutionizing the way we interact with computers in our everyday lives. Text-based games, such as the Zork series, act as testbeds for development of novel learning agents capable of understanding and interacting exclusively through language. Beyond requiring the use of imagination and myriad concepts of everyday life to solve, these fictional world–based narratives are also a safe sandbox for AI testing that avoids the expense of collecting user data and the risk of users having a bad experience interacting with agents that are still learning.
In this blog post, we share two papers that explore reinforcement learning methods to improve semantic understanding in text