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Monte Carlo Tree Search

John Levine

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  • Recommended by 1 person: 1 in engineering communities.
  • Presented by John Levine, University of Strathclyde
  • May be out of date. Uploaded 9 years ago, in an area where tools change fast.

Intermediate. Assumes working knowledge.

215K views9 years ago1 recommendation

Computer scienceAI and machine learningAlgorithmsClassic machine learning

John Levine explains the Monte Carlo Tree Search algorithm with a worked example for an artificial intelligence course.

This is a video I made for my class "CS310: Foundations of Artificial Intelligence" at the University of Strathclyde. The video has a brief description of the Monte Carlo Tree Search algorithm and includes a worked example.

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1 person recommends this

What people say. Recommended as a clear explanation of Monte Carlo Tree Search for game decision-making.

u/Lj101in r/programming • Recommends

On “The Mathematics of 2048: Optimal Play with Markov Decision Processes”

So you start somewhere and you're trying to find where to go next. In 2048 we only have four possible moves, so this isn't too hard for MCTS, if there were more moves to search we'd use UCB1 which is similar to simulated annealing. Anyway, we sample a move once by initially doing it, sliding the board down for example, then making a number of random moves to a certain tree depth then storing the score. This sounds silly, but if we do this many times for a move, then we've made a decent estimation of how good it is. The move with the best average score over all these random iterations that started with that move is the move we make next. We can tweak performance by changing how deep we se…
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