基于自博弈深度强化学习的黑白棋系统.zip

# 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> ​