Learning How Not to Act in Text-based Games

Matan Haroush, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor

Abstract: Large actions spaces impede an agent's ability to learn, especially when many of the actions are redundant or irrelevant. This is especially prevalent in text-based domains. We present the action-elimination architecture which combines the generalization power of Deep Reinforcement Learning and the natural language capabilities of NLP architectures to eliminate unnecessary actions and solves quests in the text-based game of Zork, significantly outperforming the baseline agents.

Keywords: Deep Reinforcement Learning, Natural Language Processing

TL;DR: A DRL agent that learns to eliminate actions in order to solve text-based games with large action spaces