  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.
