- #CODE CHICKEN CORE 2.3.4 INSTALL#
- #CODE CHICKEN CORE 2.3.4 CODE#
- #CODE CHICKEN CORE 2.3.4 DOWNLOAD#
- #CODE CHICKEN CORE 2.3.4 WINDOWS#
multi_process_num: Number of process to generate self-play data. nb_game_in_file,max_file_num: The max game number of training data is nb_game_in_file * max_file_num. If you find a good parameter set, please share in the github issues! PlayDataConfig other important hyper-parameters (I think) PlayDataConfig#save_policy_of_tau_1 = True means that the saved policy's tau is always 1. It seems that policy(π) data saved by self-play are distribution in proportion to pow(N, 1/tau).Īfter the middle of the game, the tau becomes 0, so the distribution is one-hot. 
Not use Evaluator (the newest model is selected as self-play's model). PlayWithHumanConfig#use_newest_next_generation_model = True. Pla圜onfig#use_newest_next_generation_model = True. Execute Evaluator to select the best model. PlayWithHumanConfig#use_newest_next_generation_model = False. Pla圜onfig#use_newest_next_generation_model = False. It is able to change these methods by configuration. I think the main difference between 'AlphaGo Zero' and 'AlphaZero' is whether using eval or not. download_model.sh 5 Configuration 'AlphaGo Zero' method and 'AlphaZero' method #CODE CHICKEN CORE 2.3.4 INSTALL#
Install the libraries From either the Anaconda prompt or from a command window in the top level folder where you put this distribution, enter the following.
#CODE CHICKEN CORE 2.3.4 WINDOWS#
Since we need Python 3.5 (required by the windows version of tensorflow), use your editor's search feature to find every occurrence of an f-string and rewrite it using string.format().
#CODE CHICKEN CORE 2.3.4 CODE#
The python source code for this project uses numerous f-strings, a feature new to Python 3.6. Double-click on the downloaded file to run the installer.
#CODE CHICKEN CORE 2.3.4 DOWNLOAD#
You could install the entire 2015 version (not the 2017 version that Microsoft tries to force on you) of Visual Studio but this is a large download and install, most of which you don't need. The direct download option installs Python in (I believe) C:\Users\\AppData\Local\Program\Python. Anaconda gets installed in C:\ProgramData\Anaconda3.
To access them, you first have to go to the Control Panel, select Folder Options, and on the View tab, click on the circle next to "Show hidden files, folders, or drives" in the Advanced settings section. Note: For some strange reason, both Python 3.5 and Anaconda get installed in a hidden folder.
Anaconda with Python 3.5 (Recommended) instructions. Install the 64-bit version of Python 3.5 (the 32-bit version is not sufficient). Note: Windows uses backslashes not forward slashes in path names.Ĭhange the first line (if necessary) of "src\reversi_zero\agent\player.py" to This instruction is written by Thanks! Required: 64-bit windows Procedure verified for Windows 8.1. If you want to train the model from the beginning, delete the above directories. data/play_data/play_*.json: generated training data. data/model/next_generation/*: next-generation models. play_gui is Play Game vs BestModel using wxPython. (It is AlphaZero method)įor evaluation, you can play reversi with the BestModel. If _newest_next_generation_model = True, this worker is useless. eval is Evaluator to evaluate whether the next-generation model is better than BestModel. opt is Trainer to train model, and generate next-generation models. self is Self-Play to generate training data by self-play using BestModel. This AlphaGo Zero implementation consists of three worker self, opt and eval. When play_gui, tensorflow(cpu) is enough speed. tensorflow=1.3.0 is also ok, but very slow. If you can share your achievements, I would be grateful if you post them to Performance Reports. Reversi reinforcement learning by AlphaGo Zero training hisotry is Challenge History.
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