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Learning How Not to Act in Text-based Games
Matan Haroush, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor
Feb 12, 2018 (modified: Jun 04, 2018)ICLR 2018 Workshop Submissionreaders: everyoneShow Bibtex
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
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