Integrating Acting, Planning and Learning in Hierarchical Operational Models
This work addresses the challenge of integrating acting, planning, and learning for hierarchical operational models, which is incremental as it builds upon existing RAE frameworks.
The authors tackled the problem of improving task performance in dynamic environments by developing new planning and learning algorithms for the Refinement Acting Engine (RAE), resulting in significant improvements in efficiency and success ratio across four test domains.
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.