413 research outputs found
A Survey of Monte Carlo Tree Search Methods
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 interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
Quoridor agent using Monte Carlo Tree Search
This thesis presents a preliminary study using Monte Carlo Tree Search (MCTS) upon the board game of Quoridor. The system is shown to perform well against current existing methods, defeating a set of player agents drawn from an existing digital implementation
Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups
Monte Carlo Tree Search (MCTS) has improved the performance of game engines
in domains such as Go, Hex, and general game playing. MCTS has been shown to
outperform classic alpha-beta search in games where good heuristic evaluations
are difficult to obtain. In recent years, combining ideas from traditional
minimax search in MCTS has been shown to be advantageous in some domains, such
as Lines of Action, Amazons, and Breakthrough. In this paper, we propose a new
way to use heuristic evaluations to guide the MCTS search by storing the two
sources of information, estimated win rates and heuristic evaluations,
separately. Rather than using the heuristic evaluations to replace the
playouts, our technique backs them up implicitly during the MCTS simulations.
These minimax values are then used to guide future simulations. We show that
using implicit minimax backups leads to stronger play performance in Kalah,
Breakthrough, and Lines of Action.Comment: 24 pages, 7 figures, 9 tables, expanded version of paper presented at
IEEE Conference on Computational Intelligence and Games (CIG) 2014 conferenc
Temporal Difference Learning in Complex Domains
PhDThis thesis adapts and improves on the methods of TD(k) (Sutton 1988) that were
successfully used for backgammon (Tesauro 1994) and applies them to other complex
games that are less amenable to simple pattem-matching approaches. The games
investigated are chess and shogi, both of which (unlike backgammon) require
significant amounts of computational effort to be expended on search in order to
achieve expert play. The improved methods are also tested in a non-game domain.
In the chess domain, the adapted TD(k) method is shown to successfully learn the
relative values of the pieces, and matches using these learnt piece values indicate that
they perform at least as well as piece values widely quoted in elementary chess books.
The adapted TD(X) method is also shown to work well in shogi, considered by many
researchers to be the next challenge for computer game-playing, and for which there
is no standardised set of piece values.
An original method to automatically set and adjust the major control parameters used
by TD(k) is presented. The main performance advantage comes from the learning
rate adjustment, which is based on a new concept called temporal coherence.
Experiments in both chess and a random-walk domain show that the temporal
coherence algorithm produces both faster learning and more stable values than both
human-chosen parameters and an earlier method for learning rate adjustment.
The methods presented in this thesis allow programs to learn with as little input of
external knowledge as possible, exploring the domain on their own rather than by
being taught. Further experiments show that the method is capable of handling many
hundreds of weights, and that it is not necessary to perform deep searches during the
leaming phase in order to learn effective weight
Temoral Difference Learning in Complex Domains
Submitted to the University of London for the Degree of Doctor of Philosophy in Computer Scienc
- …