- This project uses a model-based training that is similar to alpha-zero to teach an agent to play a environments that non-stricly alternating players between turns , mainly english draughts and turkish draughts ( dama )
- python 3.10.4 environment
- git
-
download or clone this repo
git clone https://github.com/rusenburn/python-checkers-bot -
Install python libraries using requirements.txt file
pip install -r requirements.txtorpip3 install -r requirements.txt -
For now you have to create a tmp folder inside the project folder
-
inside
main.py
from checkers_zero.trainers import AlphaZeroTrainer
from checkers_zero.english_draughts import EnglishDraughtsEnv
from checkers_zero.helpers import get_device
import random
import os
def game_fn():
if random.random() < 0.5:
return EnglishDraughtsEnv(all_kings_mode=False)
return EnglishDraughtsEnv(all_kings_mode=True)
def test_game_fn():
return EnglishDraughtsEnv(all_kings_mode=False)
def main():
game = game_fn()
network = SharedResNetwork(
shape=game.observation_space,
n_actions= game.n_actions,
n_blocks=5,
filters=128)
# If you wanna load a previously trained network weights
# path = os.path.join("tmp","english_draught_alpha_zero.pt")
# network.load_model(path)
network.to(device=device)
trainer = AlphaZeroTrainer(
game_fn=game_fn,
n_iterations=20,
n_episodes=128,
n_sims=100,
n_epochs=4,
n_batches=8,
lr=2.5e-4,
actor_critic_ratio=0.5,
n_testing_episodes=20,
network=network,
use_async_mcts=True,
use_mp=True,
test_game_fn=test_game_fn)
## trainer.train returns an network iterator
for nn in trainer.train(): iterator
path = os.path.join("tmp", "english_draught_alpha_zero.pt")
# save model after each iteration
nn.save_model(path)- run
python main.pyorpython3 main.py
- Setup.py still not developed
- No documentation yet
- The trainer takes environment function as a parameter which cannot be a lambda function
- UI is not perfect and should be manually used
- Turkish draughts flying king can 180 degree jump even if is illegal