Structure and Randomness in Planning and Reinforcement Learning

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.