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.
@article{arxiv.2109.09968,
title = {Generalization in Text-based Games via Hierarchical Reinforcement Learning},
author = {Yunqiu Xu and Meng Fang and Ling Chen and Yali Du and Chengqi Zhang},
journal= {arXiv preprint arXiv:2109.09968},
year = {2021}
}