论文标题
调整计划策略以适应强化学习者
Adjust Planning Strategies to Accommodate Reinforcement Learning Agents
论文作者
论文摘要
在代理控制问题中，结合加强学习和计划的想法引起了很多关注。两种方法分别侧重于微观和宏观作用。如果他们之间有良好的合作，他们的优势将共同显示。合作的必要条件是找到一个适当的边界，为每种方法分配不同的功能。该边界可以用计划算法中的参数表示。在本文中，我们通过分析反应和计划的联系来创建计划参数的优化策略；我们还创建了一种非毕业方法来加速优化。整个算法可以找到令人满意的计划参数设置，从而充分利用特定代理的反应能力。
In agent control issues, the idea of combining reinforcement learning and planning has attracted much attention. Two methods focus on micro and macro action respectively. Their advantages would show together if there is a good cooperation between them. An essential for the cooperation is to find an appropriate boundary, assigning different functions to each method. Such boundary could be represented by parameters in a planning algorithm. In this paper, we create an optimization strategy for planning parameters, through analysis to the connection of reaction and planning; we also create a non-gradient method for accelerating the optimization. The whole algorithm can find a satisfactory setting of planning parameters, making full use of reaction capability of specific agents.
