D. Chris Rayner, Katherine Davison, Vadim Bulitko, Kenneth Anderson, Jieshan Lu
Learning real-time search, which interleaves planning and acting, allows agents to learn from multiple trials and respond quickly. Such algorithms require no prior knowledge of the environment and can be deployed without pre-processing. We introduce Prioritized-LRTA* (P-LRTA*), a learning real-time search algorithm based on Prioritized Sweeping. P-LRTA* focuses learning on important areas of the search space, where the importance of a state is determined by the magnitude of the updates made to neighboring states. Empirical tests on path-planning in commercial game maps show a substantial learning speed-up over state-of-the-art real-time search algorithms.
Subjects: 15.7 Search; 1.11 Planning
Submitted: Oct 16, 2006