Real-time heuristic search with a priority queue

摘要: Learning real-time search, which interleaves planning and acting, allows agents to learn from multiple trials respond quickly. Such algorithms require no prior knowledge of the environment can be deployed without pre-processing. We introduce Prioritized-LRTA* (P-LRTA*), a learning search algorithm based on Prioritized Sweeping. P-LRTA* focuses important areas space, where importance state is determined by magnitude updates made neighboring states. Empirical tests path-planning in commercial game maps show substantial speed-up over state-of-the-art algorithms.