Abstract:
Planning in large state spaces inevitably needs to balance the depth and breadth of the search. It has a crucial impact on the performance of a planner and most manage th...Show MoreMetadata
Abstract:
Planning in large state spaces inevitably needs to balance the depth and breadth of the search. It has a crucial impact on the performance of a planner and most manage this interplay implicitly. We present a novel method Shoot Tree Search (STS), which makes it possible to control this trade-off more explicitly. Our algorithm can be understood as an interpolation between two celebrated search mechanisms: MCTS and random shooting. It also lets the user control the bias-variance trade-off, akin to TD(n), but in the tree search context. In experiments on challenging domains, we show that STS can get the best of both worlds consistently achieving higher scores.
Date of Conference: 18-22 July 2021
Date Added to IEEE Xplore: 20 September 2021
ISBN Information: