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2048-TD_Lambda.cpp Source Code Apr 10, 2015
GameAI.cpp Source Code Apr 10, 2015
GameAI.h Source Code Apr 10, 2015
LICENSE.md Update LICENSE.md Jul 25, 2015
README.md Update README.md Oct 23, 2017
TDLearning.h Source Code Apr 10, 2015
bitboard.cpp Source Code Apr 10, 2015
bitboard.h Source Code Apr 10, 2015
config.cpp Source Code Apr 10, 2015
config.h Source Code Apr 10, 2015
feature.h Source Code Apr 10, 2015
featureSet.h Source Code Apr 10, 2015
operation.h Source Code Apr 10, 2015
pruneEmm.h Source Code Apr 10, 2015
rng.h Source Code Apr 10, 2015
tupleNetwork.h Source Code Apr 10, 2015
zhash.h Source Code Apr 10, 2015

README.md

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/