Learning How Not to Act in Text-based Games

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.