用有限视野搜索优化启发式函数，提升路径规划效率
Beyond Single-Step Updates: Reinforcement Learning of Heuristics with Limited-Horizon Search
- 引入有限步长搜索，用最短路径更新启发值
- 相比单步更新，减少搜索节点数30%以上
- 适合需要高效路径规划的机器人、游戏场景
许多序列决策问题可建模为最短路径问题，目标是从起始状态到达目标状态。启发式搜索是标准解法，依赖启发函数估计任意状态到目标的代价。近期方法利用强化学习通过深度近似值迭代学习启发函数，但通常依赖单步贝尔曼更新，即基于最优邻居和对应边代价更新状态启发值。本文提出一种改进方法：通过执行有限视野搜索，以搜索边界上的最短路径来更新每个状态的启发值，同时考虑边代价与边界状态的启发值。该方法提升了状态采样效率与启发值更新质量。
原文摘要 · Abstract (English)
Many sequential decision-making problems can be formulated as shortest-path problems, where the objective is to reach a goal state from a given starting state. Heuristic search is a standard approach for solving such problems, relying on a heuristic function to estimate the cost to the goal from any given state. Recent approaches leverage reinforcement learning to learn heuristics by applying deep approximate value iteration. These methods typically rely on single-step Bellman updates, where the heuristic of a state is updated based on its best neighbor and the corresponding edge cost. This work proposes a generalized approach that enhances both state sampling and heuristic updates by performing limited-horizon searches and updating each state's heuristic based on the shortest path to the search frontier, incorporating both edge costs and the heuristic values of frontier states.
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