Weighted real-time heuristic search
Nicolás Rivera,Jorge A. Baier,Carlos Hernández +2 more
- pp 579-586
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TLDR
This paper presents two novel approaches to using weights in RTHS, one of which is a variant of a previous approach by Shimbo and Ishida and the other incorporates the weight to the edges of the search graph during the learning phase.Abstract:
Multiplying the heuristic function by a weight greater than one is a well-known technique in Heuristic Search. When applied to A* with an admissible heuristic it yields substantial runtime savings, at the expense of sacrificing solution optimality. Only a few works have studied the applicability of this technique to Real-Time Heuristic Search (RTHS), a search approach that builds upon Heuristic Search. In this paper we present two novel approaches to using weights in RTHS. The first one is a variant of a previous approach by Shimbo and Ishida. It incorporates weights to the lookahead search phase of the RTHS algorithm. The second one incorporates the weight to the edges of the search graph during the learning phase. Both techniques are applicable to a wide class of RTHS algorithms. Here we implement them within LSS-LRTA* and LRTA*-LS, obtaining a family of new algorithms. We evaluate them in path-planning benchmarks and show the second technique yields improvements of up to one order-of-magnitude both in solution cost and total search time. The first technique, on the other hand, yields poor results. Furthermore, we prove that RTHS algorithms that can appropriately use our second technique terminate finding a solution if one exists.read more
Citations
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Proceedings ArticleDOI
A Survey on Path Planning Algorithms for Mobile Robots
Marcia M. Costa,Manuel Silva +1 more
TL;DR: This study was developed in order to implement some of these path planning algorithms in the near future, with the objective to find out their relative advantages and disadvantages, and in which situations their implementation is more adequate.
Journal ArticleDOI
A new weighted pathfinding algorithms to reduce the search time on grid maps
TL;DR: Three new weight techniques for pathfinding algorithm using various weights to accelerate heuristic search of grid maps are proposed and implemented within the A*, the Bidirectional A* (Bi-A*) and Jump Point Search (JPS) algorithms, obtaining a family of new algorithms.
Journal ArticleDOI
Incorporating weights into real-time heuristic search
TL;DR: It is shown that weighted lookahead outperforms an existing approach by Shimbo and Ishida but that it does not improve over existing approaches that do not use weights, and the generality of weighted update is incorporated in two other well-known real-time heuristic search algorithms: LRTA*-LS and daLSS-LRTA*, and it is proved solutions are w-optimal, and additional bounds on solution quality that in practice are tighter than w-optimality.
Proceedings ArticleDOI
Avoiding moving obstacles with stochastic hybrid dynamics using PEARL: PrEference Appraisal Reinforcement Learning
TL;DR: It is demonstrated that on a dynamic obstacle avoidance robotic task, a single learning on a much simpler problem performs real-time decision-making for significantly larger, high-dimensional problems working in unbounded continuous states and actions.
Journal ArticleDOI
Achieving goals quickly using real-time search: experimental results in video games
TL;DR: This work investigates several enhancements to a leading real-time search algorithm, LSS-LRTA*, and shows experimentally that it is better to plan after each action or to use a dynamically sized lookahead, which can cause undesirable actions to be selected and on-line de-biasing of the heuristic can lead to improved performance.
References
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Proceedings Article
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