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arXiv · 2109.09968

Generalization in Text-based Games via Hierarchical Reinforcement Learning

Yunqiu Xu·Meng Fang·Ling Chen·Yali Du·Chengqi Zhang
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Abstract

Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the generalization still remains a big challenge as the agents depend critically on the complexity and variety of training tasks. In this paper, we address this problem by introducing a hierarchical framework built upon the knowledge graph-based RL agent. In the high level, a meta-policy is executed to decompose the whole game into a set of subtasks specified by textual goals, and select one of them based on the KG. Then a sub-policy in the low level is executed to conduct goal-conditioned reinforcement learning. We carry out experiments on games with various difficulty levels and show that the proposed method enjoys favorable generalizability.

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Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Chengqi Zhang. 2021-09-21. Generalization in Text-based Games via Hierarchical Reinforcement Learning. https://arxiv.org/abs/2109.09968

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