Structure and randomness in planning and reinforcement learning

Piotr Kozakowski, Piotr Januszewski, Konrad Czechowski, Łukasz Kuciński, Piotr Miłoś

Keywords: reinforcement learning, uncertainty, model-based, MCTS

TL;DR: We present a novel planning algorithm based on modern version of MCTS (with neural-network heuristics).

Abstract:

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.