Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning

Tongzhou Mu, Kaixiang Lin, Feiyang Niu, Govind Thattai

Abstract: 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.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Michael_Bowling1

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Submission Number: 342