2020/03/09 by Sunandita Patra, James Mason, Patra, Sunandita +9 · 1 citation
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Robotic Path Planning Algorithms #Spreadsheets and End-User Computing #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2003.03932
Accepted in ICAPS 2020 (30th International Conference on Automated Planning and Scheduling)
arxiv created 2020/03/09 · openalex publication_date 2020/03/09 · arxiv updated 2020/03/10 · openalex created_date 2022/09/27 · openalex updated_date 2026/07/28
We present new planning and learning algorithms for RAE, the Refinement Acting Engine. RAE uses hierarchical operational models to perform tasks in dynamically changing environments. Our planning procedure, UPOM, does a UCT-like search in the space of operational models in order to find a near-optimal method to use for the task and context at hand. Our learning strategies acquire, from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. Our experimental results show that UPOM and our learning strategies significantly improve RAE's performance in four test domains using two different metrics: efficiency and success ratio.