TY - JOUR AU - Kokel, Harsha AU - Lee, Junkyu AU - Katz, Michael AU - Sohrabi, Shirin AU - Srinivas, Kavitha PY - 2022/06/28 Y2 - 2024/03/29 TI - How to Reduce Action Space for Planning Domains? (Student Abstract) JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 11 SE - AAAI Student Abstract and Poster Program DO - 10.1609/aaai.v36i11.21631 UR - https://ojs.aaai.org/index.php/AAAI/article/view/21631 SP - 12989-12990 AB - While AI planning and Reinforcement Learning (RL) solve sequential decision-making problems, they are based on different formalisms, which leads to a significant difference in their action spaces. When solving planning problems using RL algorithms, we have observed that a naive translation of the planning action space incurs severe degradation in sample complexity. In practice, those action spacesare often engineered manually in a domain-specific manner. In this abstract, we present a method that reduces the parameters of operators in AI planning domains by introducing a parameter seed set problem and casting it as a classical planning task. Our experiment shows that our proposed method significantly reduces the number of actions in the RL environments originating from AI planning domains. ER -