Pre-trained Language Models as Prior Knowledge for Playing Text-based Games

• URL: http://arxiv.org/abs/2107.08408v1
• Date: Sun, 18 Jul 2021 10:28:48 GMT
• Title: Pre-trained Language Models as Prior Knowledge for Playing Text-based Games
• Authors: Ishika Singh and Gargi Singh and Ashutosh Modi
• Abstract summary: In this paper, we improve the semantic understanding of the agent by proposing a simple RL with LM framework. We perform a detailed study of our framework to demonstrate how our model outperforms all existing agents on the popular game, Zork1. Our proposed approach also performs comparably to the state-of-the-art models on the other set of text games.
• Score: 2.423547527175808
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, text world games have been proposed to enable artificial agents to understand and reason about real-world scenarios. These text-based games are challenging for artificial agents, as it requires understanding and interaction using natural language in a partially observable environment. In this paper, we improve the semantic understanding of the agent by proposing a simple RL with LM framework where we use transformer-based language models with Deep RL models. We perform a detailed study of our framework to demonstrate how our model outperforms all existing agents on the popular game, Zork1, to achieve a score of 44.7, which is 1.6 higher than the state-of-the-art model. Our proposed approach also performs comparably to the state-of-the-art models on the other set of text games.

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