用关键节点指导随机规划，提升算法效率。
Landmark-Assisted Monte Carlo Planning
- 将确定性规划中的关键点概念扩展到随机环境，作为子目标
- 在基准测试中显著提升UCT算法性能，最优平衡依赖具体问题
- 适合需要实时决策的马尔可夫决策过程求解场景
地标——所有解计划中必须满足的条件——在经典规划中推动了重大进展，但在随机领域应用较少。本文形式化了概率地标，并改进UCT算法，将其作为子目标分解MDP；核心在于平衡贪婪达成地标与最终目标达成之间的关系。在基准域上的实验表明，精心选择的地标能显著提升UCT在在线随机规划中的表现，而贪婪与长期目标之间的最佳平衡取决于具体问题。结果表明，地标可为求解MDP的任意时算法提供有效引导。
原文摘要 · Abstract (English)
Landmarks$\unicode{x2013}$conditions that must be satisfied at some point in every solution plan$\unicode{x2013}$have contributed to major advancements in classical planning, but they have seldom been used in stochastic domains. We formalize probabilistic landmarks and adapt the UCT algorithm to leverage them as subgoals to decompose MDPs; core to the adaptation is balancing between greedy landmark achievement and final goal achievement. Our results in benchmark domains show that well-chosen landmarks can significantly improve the performance of UCT in online probabilistic planning, while the best balance of greedy versus long-term goal achievement is problem-dependent. The results suggest that landmarks can provide helpful guidance for anytime algorithms solving MDPs.
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