Despite the recent advancement in deep reinforcement learning (DRL), it still struggles at learning sparse-reward goal-directed tasks. On the other hand, classical planning approaches excel at addressing tasks with hierarchical structures by employing symbolic knowledge for high-level planning. Yet, most classical planning methods rely on assumptions about pre-defined subtasks, making them inapplicable in domains without domain knowledge or models. To bridge the best of both worlds, we propose a framework that integrates DRL with classical planning by automatically inducing task structures and substructures from a few demonstrations. Specifically, we use symbolic regression for substructure induction by adopting genetic programming where the program model reflects prior domain knowledge of effect rules. We compare our proposed framework to DRL algorithms, imitation learning methods, and an exploration approach in various domains. The experimental results show that our framework outperforms the baselines in terms of sample efficiency and task performance. Moreover, our framework achieves strong generalization performance by inducing the new rules and composing the task structures. Ablation studies justify the design of the induction module and the proposed genetic programming procedure.
Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures
Jung-Chun Liu Chi-Hsien Chang Shao-Hua Sun Tian-Li Yu