Monte Carlo Tree Search

Monte-Carlo Tree Search (MCTS) is a method for finding the optimal path through a decision process.

It is used in scenarios where the decision space is large and complex, such as game playing and planning problems. The algorithm works by simulating many random paths through the decision process and recording the outcomes. Each node in the decision tree collects information about the success rate of paths passing through it. Over time, this information helps to guide future decisions towards more promising paths.

For example, in a game, MCTS can simulate many possible moves and counter-moves, learning which sequences lead to victory. This allows it to make informed decisions even in complex and uncertain environments.

The main features of MCTS are its ability to balance exploration and exploitation, and its applicability to a wide range of problems. It is important because it provides a robust framework for decision-making in uncertain and dynamic environments.

UCT (Upper Confidence Bound 1 applied to trees) is a variant of MCTS that improves efficiency by balancing exploration and exploitation during the search process.