Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa,
superhuman proficiency in challenging domains. Recently, AlphaGo became the first …

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 …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

We present a state of the art implementation of the Monte Carlo Tree Search algorithm for
the game of Go. Our Pachi software is currently one of the strongest open source Go …

The game of Go has long been viewed as the most challenging of classic games for artificial
intelligence owing to its enormous search space and the difficulty of evaluating board …

Algorithms based on upper confidence bounds for balancing exploration and exploitation
are gaining popularity since they are easy to implement, efficient and effective. This paper …

A new paradigm for search, based on Monte-Carlo simulation, has revolutionised the
performance of computer Go programs. In this article we describe two extensions to the …

We consider a generalization of stochastic bandits where the set of arms, X, is allowed to be
a generic measurable space and the mean-payoff function is “locally Lipschitz” with respect …

We consider the framework of stochastic multi-armed bandit problems and study the
possibilities and limitations of strategies that perform an online exploration of the arms. The …