We present a two-step hybrid reinforcement learning (RL) policy that is
designed to generate interpretable and robust hierarchical policies on the RL
problem with graph-based input. Unlike prior deep reinforcement learning
policies parameterized by an end-to-end black-box graph neural network, our
approach disentangles the decision-making process into two steps. The first
step is a simplified classification problem that maps the graph input to an
action group where all actions share a similar semantic meaning. The second
step implements a sophisticated rule-miner that conducts explicit one-hop
reasoning over the graph and identifies decisive edges in the graph input
without the necessity of heavy domain knowledge. This two-step hybrid policy
presents human-friendly interpretations and achieves better performance in
terms of generalization and robustness. Extensive experimental studies on four
levels of complex text-based games have demonstrated the superiority of the
proposed method compared to the state-of-the-art.

本文提出了一种基于图形输入的两步式 RL 策略，透过实用化分类问题将决策过程分解为两个步骤并采用规则挖掘器进行推理，能够生成可解释和鲁棒的分层策略并在复杂文本游戏领域呈现出更好的泛化和稳健性。