Monte Carlo Tree Search is an incredibly powerful tool, it requires no domain knowledge and yet it can produce state of the art results. A common application of Monte Carlo Tree Search is games that can be played to completion such as Go, however this project aims to apply Monte Carlo Tree Search to the game 2048 – which has a substantial search depth, making traditional approaches unfeasible. To overcome this challenge, a genetic algorithm is used to find a near optimal combination of search depth and number of runs. While more computing power generally leads to better results, these methods show that a near optimal solution can be found for a fraction of the computing requirements.
This project includes modified code from the python package Term2048.
python play.py mcts
This project was created as part of class project on artificial intelligence.