Monte Carlo tree search

In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in game play. MCTS was introduced in 2006 for computer Go. It has been used in other board games like chess and shogi, games with incomplete information such as bridge and poker, as well as in real-time video games (such as Total War: Rome II's implementation in the high level campaign AI). In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in game play. MCTS was introduced in 2006 for computer Go. It has been used in other board games like chess and shogi, games with incomplete information such as bridge and poker, as well as in real-time video games (such as Total War: Rome II's implementation in the high level campaign AI). The Monte Carlo method, which uses randomness for deterministic problems difficult or impossible to solve using other approaches, dates back to the 1940s. In his 1987 PhD thesis, Bruce Abramson combined minimax search with an expected-outcome model based on random game playouts to the end, instead of the usual static evaluation function. Abramson said the expected-outcome model 'is shown to be precise, accurate, easily estimable, efficiently calculable, and domain-independent.' He experimented in-depth with Tic-tac-toe and then with machine-generated evaluation functions for Othello and Chess. Such methods were then explored and successfully applied to heuristic search in the field of automated theorem proving by W. Ertel, J. Schumann and C. Suttner in 1989, thus improving the exponential search times of uninformed search algorithms such as e.g. breadth-first search, depth-first search or iterative deepening. In 1992, B. Brügmann employed it for the first time in a Go-playing program. Chang et al. proposed the idea of 'recursive rolling out and backtracking' with 'adaptive' sampling choices in their Adaptive Multi-stage Sampling (AMS) algorithm for the model of Markov decision processes. AMS was the first work to explore the idea of UCB-based exploration and exploitation in constructing sampled/simulated (Monte Carlo) trees and was the main seed for UCT (Upper Confidence Trees). In 2006, inspired by these predecessors, Rémi Coulom described the application of the Monte Carlo method to game-tree search and coined the name Monte Carlo tree search, L. Kocsis and Cs. Szepesvári developed the UCT algorithm, and S. Gelly et al. implemented UCT in their program MoGo. In 2008, MoGo achieved dan (master) level in 9×9 Go, and the Fuego program began to win against strong amateur players in 9×9 Go. In January 2012, the Zen program won 3:1 in a Go match on a 19×19 board with an amateur 2 dan player. Google Deepmind developed the program AlphaGo, which in October 2015 became the first Computer Go program to beat a professional human Go player without handicaps on a full-sized 19x19 board. In March 2016, AlphaGo was awarded an honorary 9-dan (master) level in 19×19 Go for defeating Lee Sedol in a five-game match with a final score of four games to one. AlphaGo represents a significant improvement over previous Go programs as well as a milestone in machine learning as it uses Monte Carlo tree search with artificial neural networks (a deep learning method) for policy (move selection) and value, giving it efficiency far surpassing previous programs. Monte Carlo tree search has also been used in programs that play other board games (for example Hex, Havannah, Game of the Amazons, and Arimaa), real-time video games (for instance Ms. Pac-Man and Fable Legends), and nondeterministic games (such as skat, poker, Magic: The Gathering, or Settlers of Catan). The focus of Monte Carlo tree search is on the analysis of the most promising moves, expanding the search tree based on random sampling of the search space.The application of Monte Carlo tree search in games is based on many playouts. In each playout, the game is played out to the very end by selecting moves at random. The final game result of each playout is then used to weight the nodes in the game tree so that better nodes are more likely to be chosen in future playouts. The most basic way to use playouts is to apply the same number of playouts after each legal move of the current player, then choose the move which led to the most victories. The efficiency of this method—called Pure Monte Carlo Game Search—often increases with time as more playouts are assigned to the moves that have frequently resulted in the current player's victory according to previous playouts. Each round of Monte Carlo tree search consists of four steps: