提出高效采样规划算法，仅用少量样本逼近最优策略。
Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning
- 基于近优策略路径的结构化采样，减少无效探索
- 理论保证样本复杂度与近优状态数相关
- 兼顾计算效率与可实现性，适合实时决策场景
你在马尔可夫决策过程（MDP）中是一名有智能的机器人，拥有生成模型以进行蒙特卡洛规划。面对时间紧迫的现实世界，你希望规划过程高效且样本节省。核心思想是利用近优策略可达状态的潜在结构，仅采样部分关键状态。该方法将蒙特卡洛采样推广至包含动作最大化与状态期望交替的规划问题，同时避免指数级计算开销。通过理论分析，其样本复杂度依赖于近优状态的数量。算法设计简洁、易于实现，兼具理论保障与计算效率，适用于资源受限的实时决策系统。
原文摘要 · Abstract (English)
You are a robot and you live in a Markov decision process (MDP) with a finite or an infinite number of transitions from state-action to next states. You got brains and so you plan before you act. Luckily, your roboparents equipped you with a generative model to do some Monte-Carlo planning. The world is waiting for you and you have no time to waste. You want your planning to be efficient. Sample-efficient. Indeed, you want to exploit the possible structure of the MDP by exploring only a subset of states reachable by following near-optimal policies. You want guarantees on sample complexity that depend on a measure of the quantity of near-optimal states. You want something, that is an extension of Monte-Carlo sampling (for estimating an expectation) to problems that alternate maximization (over actions) and expectation (over next states). But you do not want to StOP with exponential running time, you want something simple to implement and computationally efficient. You want it all and you want it now. You want TrailBlazer.
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