论文标题
将表演，计划和学习整合到等级运营模型中
Integrating Acting, Planning and Learning in Hierarchical Operational Models
论文作者
论文摘要
我们提出了新的计划和学习算法Rae，Rae，Rae是改进的代理引擎。 RAE使用分层操作模型在动态变化的环境中执行任务。我们的计划程序UPOM在操作模型的空间中进行了类似UCT的搜索，以便找到一种近乎最佳的方法，可以用于手头的任务和上下文。我们的学习策略从在线表演经验和/或模拟计划结果中获取了从决策环境到方法实例的映射以及指导Upom的启发式功能。我们的实验结果表明，使用两个不同的指标：效率和成功率，UPOM和我们的学习策略可显着提高RAE在四个测试域中的性能。
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.
