2048 AI

Some ugly codes of TD-Learning and Expectimax Search for game 2048.

(Developed by K.H Yeh and I.C Wu from CGI-Lab @NCTU).

We use TD-Lambda and several features to train 2048 by self-playing.

Those features includes:

1. Number of large tiles

2. Number of pairs of merge-able tiles

3. Number of disintinct tiles

4. Number of empty tiles

5. Number of layered tiles (Twice larger or smaller than neighbors)

6. Axe-shape six-tuples

7. Rectangular six-tuples

The download link for the trained features weights for this program:

1. http://140.113.210.143/~cgilab/download/

2. http://140.113.210.143/~cgilab/download/2048%20features%20trained.rar

Performances: (1000 games)

The AI is on the website: http://2048.aigames.nctu.edu.tw/

To see the record of reaching 65536: http://2048.aigames.nctu.edu.tw/replay.php

Metrics	Values
Average	446116
Max	833300
2048 rate	100%
4096 rate	99.8%
8192 rate	99.5%
16384 rate	93.6%
32768 rate	33.5%
Speed	500 moves/sec
Search depth	2.5 (5)

The program's result along with other experiments are in the IEEE Journal Paper: http://ieeexplore.ieee.org/document/7518633/