Structure and randomness in planning and reinforcement learning
Planning in large state spaces inevitably needs to balance depth and breadth of the search. It has a crucial impact on planners performance and most manage this interplay implicitly. We present a novel method $\textit{Shoot Tree Search (STS)}$, 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.
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