树搜索算法的开环执行，扩展版
Open Loop Execution of Tree-Search Algorithms, extended version
摘要：
在树搜索随机规划算法的生成模型的情况下，我们考虑在线规划算法建立树，以推荐一个行动。我们调查的问题，避免重新规划在随后的决策步骤，直接使用子树作为行动推荐。首先，我们提出了一种开环控制方法，通过一种新的算法，在每一个时间步的基础上的子树的统计分析的重新规划或没有决定。其次，我们证明了在树的任何深度选择次优动作的概率都可以是上界的，并且收敛于零。此外，该上限在后续深度之间以对数方式衰减。这导致了节点最优性和状态最优性之间的区别。最后，我们经验证明，我们的方法实现了性能损失和计算增益之间的妥协。
英文摘要：
In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action. We investigate the question of avoiding re-planning in subsequent decision steps by directly using sub-trees as action recommender. Firstly, we propose a method for open loop control via a new algorithm taking the decision of re-planning or not at each time step based on an analysis of the statistics of the sub-tree. Secondly, we show that the probability of selecting a suboptimal action at any depth of the tree can be upper bounded and converges towards zero. Moreover, this upper bound decays in a logarithmic way between subsequent depths. This leads to a distinction between node-wise optimality and state-wise optimality. Finally, we empirically demonstrate that our method achieves a compromise between loss of performance and computational gain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
