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.
@article{arxiv.2003.03932,
title = {Integrating Acting, Planning and Learning in Hierarchical Operational Models},
author = {Sunandita Patra and James Mason and Amit Kumar and Malik Ghallab and Paolo Traverso and Dana Nau},
journal= {arXiv preprint arXiv:2003.03932},
year = {2020}
}
Comments
Accepted in ICAPS 2020 (30th International Conference on Automated Planning and Scheduling)