# How to run?
Dependencies : <br> numpy, <br> pytorch1.0.1.post2 (The newest edition download from <https://pytorch.org/> )<br> cuda10.0<br> CUDA 10.0.130<br> CUDNN 7.5.0.56
In the root directory, there are two source files : board.py and reversi.py, which are offered by the origin engine frame.<br>And a single directory : engines. You can get my engine interface "unispac_21.py" in the "engines" directory.<br> Run the engine by input :<br> python3 reversi.py -a eona -b unispac_21<br> Then you can see the match play between my engine and the example engine.<br> Note that in oder to take best use of the time resource, each step will cost about 30 seconds.. <br> If you want to see the result more quickly, open the source file : "unispac_21.py" and modify the args.simCntOfMCT.<br> 100~500 is recommended..
!!! Last but not least..<br> If you want to transplant my engine to another game environment. Don't forget to take along the directory "alpha" under "engines" togther.<br> The directory "alpha" offers the key module of my engine..<br> And as there may be a problem of path.. It is recommended to organize the game just like the example frame.. Put all the engine files in the engiles directory.<br>Put my "alpha" directory under the engiens dir.. And the reversi.py is under the root directory..
# About author
Developer : Qi Xiangyu(漆翔宇)<br> Sid : 3170104557<br> GroupNumber : 21<br> Major : Computer Science and Technology<br> Tel : 17342017090<br> Email : unispac@zju.edu.cn<br> QQ : 416162623<br>
Please contact with me if you have encountered any problems when running it..<br> The report will be handed in before 8:00 am on 4.21 as required.. <br>
人工智能项目资料-基于自博弈深度强化学习的黑白棋系统.zip
版权申诉
49 浏览量
2024-02-07
15:46:04
上传
评论
收藏 57.79MB ZIP 举报
基于自博弈深度强化学习的黑白棋系统.zip （52个子文件） 
资料总结 doc 
readme.md 2KB
unispac_21.pdf 855KB reversi.py 8KB engines human.py 2KB __init__.py 1KB noorder.py 14KB greedy.py 1KB random.pyc 881B alpha chessGame reversiGame.py 4KB chessBoard.py 6KB __pycache__ reversiGame.cpython-36.pyc 3KB chessBoard.cpython-36.pyc 5KB NetWork networkFrame.py 4KB gameNetwork.py 3KB __pycache__ gameNetwork.cpython-36.pyc 2KB networkFrame.cpython-36.pyc 4KB tranningFrame competition.py 1KB selfCompetition.py 7KB __pycache__ competition.cpython-36.pyc 1KB selfCompetition.cpython-36.pyc 5KB MCTS mctAgent.py 1KB MCTS.py 4KB __pycache__ MCTS.cpython-36.pyc 3KB mctAgent.cpython-36.pyc 1KB model best.pth.tar 61.22MB myUtil util.py 81B __pycache__ util.cpython-36.pyc 429B eona.pyc 14KB __init__.pyc 2KB simple.py 12KB simple2.py 13KB order.py 14KB random.py 461B eona.py 16KB __pycache__ human.cpython-36.pyc 2KB eona.cpython-36.pyc 12KB greedy.cpython-36.pyc 1KB unispac_21.cpython-36.pyc 2KB __init__.cpython-35.pyc 1KB agent.cpython-36.pyc 2KB order.cpython-36.pyc 12KB noorder.cpython-36.pyc 11KB random.cpython-35.pyc 766B __init__.cpython-36.pyc 1KB human.cpython-35.pyc 2KB nonull.cpython-36.pyc 11KB greedy.cpython-35.pyc 1KB random.cpython-36.pyc 717B nonull.py 14KB unispac_21.py 1KB readme.md 2KB board.py 7KB资源评论
