Abstract Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-
playing bots or solving sequential decision problems. The method relies on intelligent tree …

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the
precision of tree search with the generality of random sampling. It has received considerable …

Monte-Carlo Tree Search (MCTS) is a new best-first search guided by the results of Monte-
Carlo simulations. In this article, we introduce two progressive strategies for MCTS, called …

Abstract Monte-Carlo Tree Search (MCTS) is a new best-first search method that started a
revolution in the field of Computer Go. Parallelizing MCTS is an important way to increase …

We consider scenarios from the real-time strategy game StarCraft as new benchmarks for
reinforcement learning algorithms. We propose micromanagement tasks, which present the …

Game tree search in games with large branching factors is a notoriously hard problem. In
this paper, we address this problem with a new sampling strategy for Monte Carlo Tree …

This paper introduces a principled approach for the design of a scalable general
reinforcement learning agent. Our approach is based on a direct approximation of AIXI, a …

Robots are being deployed in an increasing variety of environments for longer periods of
time. As the number of robots grows, they will increasingly need to interact with other robots …

We consider the problem of tactical assault planning in real-time strategy games where a
team of friendly agents must launch an assault on an enemy. This problem offers many …

Crowdsourcing, outsourcing of tasks to a crowd of unknown people (“workers”) in an open
call, is rapidly rising in popularity. It is already being heavily used by numerous employers …