class TrainingClient:
args = dotdict({
# 'load_folder_file': ('../alphabot/temp/','checkpoint_0.pth.tar'),
'checkpoint': '../alphabot/temp/'
})
def __init__(self):
# self.trainExamplesHistory = [] # history of examples from args.numItersForTrainExamplesHistory latest iterations
self.nnet = nn()
class Client:
args = dotdict({
'load_folder_file': ('../alphabot/temp.old/','checkpoint_1.pth.tar'),
'checkpoint': '../alphabot/temp/'
})
def __init__(self):
self.trainExamplesHistory = [] # history of examples from args.numItersForTrainExamplesHistory latest iterations
self.nnet = nn()
# def getCheckpointFile(self, iteration):
# return 'checkpoint_' + str(iteration) + '.pth.tar'
# def saveTrainExamples(self, iteration):
# folder = self.args.checkpoint
# if not os.path.exists(folder):
# os.makedirs(folder)
# filename = os.path.join(folder, self.getCheckpointFile(iteration)+".examples")
# with open(filename, "wb+") as f:
# Pickler(f, protocol=pickle.HIGHEST_PROTOCOL).dump(self.trainExamplesHistory)
# f.closed
def loadTrainExamples(self):
modelFile = os.path.join(self.args.load_folder_file[0], self.args.load_folder_file[1])
examplesFile = modelFile+".examples"
if not os.path.isfile(examplesFile):
print(examplesFile)
r = input("File with trainExamples not found. Continue? [y|n]")
if r != "y":
sys.exit()
else:
print("File with trainExamples found. Read it.")
with open(examplesFile, "rb") as f:
#self.trainExamplesHistory = Unpickler(f).load()
self.trainExamplesHistory = f.read()
f.closed
# examples based on the model were already collected (loaded)
self.skipFirstSelfPlay = True
def loadModelFile(self):
modelFile = os.path.join(self.args.checkpoint, 'temp.pth.tar')
if not os.path.isfile(modelFile):
print(modelFile)
r = input("Model file not found. Continue? [y|n]")
if r != "y":
sys.exit()
else:
print("File with modelFile found. Read it.")
with open(modelFile, "rb") as f:
#self.trainExamplesHistory = Unpickler(f).load()
result = f.read()
f.closed
return result
import Arena
from MCTS import MCTS
from Game import YEET as Game
from NNet import NNetWrapper as NNet
from dotted_dict import DottedDict as dotdict
import numpy as np
import logging, psutil, os, random, functools, sys
#from multiprocessing import freeze_support
import multiprocessing as mp
args = dotdict({
'numGames': 16, # 48
'numThreads': psutil.cpu_count() # 8
})
"""
use this script to play any two agents against each other, or play manually with
any agent.
"""
class RandomPlayer():
def __init__(self, game):
self.game = game
def play(self, game_instance):
# agent = game_instance.current_player
choices = np.argwhere(self.game.getValidMoves(game_instance) == 1)
return random.choice(choices)
class HumanPlayer():
import multiprocessing as mp
import random
import string
#from utils import dotdict
from dotted_dict import DottedDict as dotdict
import time
dd = dotdict({
'numIters': 100,
'numThreads': 4,
})
# define a example function
def rand_string(length, dd, output):
""" Generates a random string of numbers, lower- and uppercase chars. """
rand_str = ''.join(random.choice(
string.ascii_lowercase
+ string.ascii_uppercase
+ string.digits)
for i in range(length))
#time.sleep(10)
output.put(rand_str)
if __name__ == '__main__':
random.seed(123)
# Define an output queue
output = mp.Queue()
# Setup a list of processes that we want to run
processes = [mp.Process(target=rand_string, args=(5, dd, output)) for x in range(4)]
from NNet import NNetWrapper as nn
#from utils import dotdict
from dotted_dict import DottedDict as dotdict
#from multiprocessing import freeze_support
import multiprocessing as mp
import logging, psutil, os, sys
args = dotdict({
'numIters': 100,
'numEps': 100,
'tempThreshold':
15, # degree of exploration in MCTS.getActionProb(). switch from temperature=1 to temperature=0 after this episode step
'updateThreshold': 0.55,
'maxlenOfQueue': 200000,
'numMCTSSims': 25, # 25 # TODO: much more sims needed?
'arenaCompare': 48,
'cpuct':
2, # degree of exploration for upper confidence bound in MCTS.search() => TODO: try 2?
'checkpoint': './temp/',
'load_model': False,
'load_folder_file': ('./temp/', 'best.pth.tar'),
'numItersForTrainExamplesHistory': 50, #20
'numThreads': psutil.cpu_count(),
'remoteTraining': True
})
if __name__ == "__main__":
#freeze_support()
# Start processes with lower priority to prevent system overload/hangs/freezes. Also set multiprocessing start method to spawn for Linux, since forking makes trouble
p = psutil.Process(os.getpid())
if sys.platform.startswith('win32'):
p.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS)
import pickle
from random import shuffle
import multiprocessing as mp
from multiprocessing import current_process, Pool
from concurrent.futures import ProcessPoolExecutor, as_completed
from tqdm import tqdm
import functools
from utils.helper import *
args = dotdict({
'numIters': 100,
'numEps': 2,
'tempThreshold':
15, # degree of exploration in MCTS.getActionProb(). switch from temperature=1 to temperature=0 after this episode step
'maxlenOfQueue': 200000,
'numMCTSSims': 25, # 25 # TODO: much more sims needed?
'cpuct':
2, # degree of exploration for upper confidence bound in MCTS.search() => TODO: try 2?
'modelspath': './models/',
'examplespath': './examples/',
'numThreads': 1 #psutil.cpu_count(),
})
class Coach:
"""
This class executes the self-play + learning. It uses the functions defined
in Game and NeuralNet. args are specified in main.py.
"""
def __init__(self, game, nnet, args):
self.game = game
import pickle
import torch
import torch.optim as optim
# from torch.autograd import Variable
# from alphanet import DQN as nnet
from alphanet12 import DQN as nnet
# from alphanet6 import DQN as nnet
# from alphanet18 import DQN as nnet
# from alphanet26 import DQN as nnet
args = dotdict({
'lr': 0.0001, # 0.001 - 0.0001
# 'dropout': 0.3, # 0.3
# 'epochs': 2, # best 25
'batch_size': 64, # best 128 - 4
'cuda': True,
# 'num_channels': 512,
})
class NNetWrapper():
def __init__(self):
self.nnet = nnet(args)
if args.cuda:
self.nnet.cuda()
def train(self, examples):
"""
examples: list of examples, each example is of form (state, pi, v)
import pickle
import torch
import torch.optim as optim
# from torch.autograd import Variable
# from alphanet import DQN as nnet
from alphanet12 import DQN as nnet
# from alphanet6 import DQN as nnet
# from alphanet18 import DQN as nnet
# from alphanet26 import DQN as nnet
args = dotdict({
'lr': 0.001,
# 'dropout': 0.3,
'epochs': 10, # best 25
'batch_size': 128, # best 128
'cuda': True,
# 'num_channels': 512,
})
class NNetWrapper():
def __init__(self):
self.nnet = nnet(args)
if args.cuda:
self.nnet.cuda()
def train(self, examples):
"""
examples: list of examples, each example is of form (state, pi, v)
from pickle import Pickler, Unpickler, loads
from NNet import NNetWrapper as nn
from dotted_dict import DottedDict as dotdict
from random import shuffle
import time, os, sys
import torch
from sklearn.model_selection import train_test_split
#from tensorboard_logger import configure, log_value
from tensorboardX import SummaryWriter
args = dotdict({
'modelspath': './models/',
'examplespath': './examples/',
'epochs': 100, # best 25,
'validation': True,
'early_stopping': False,
'save_model': True
})
class TrainingClient:
def __init__(self):
# self.trainExamplesHistory = [] # history of examples from args.numItersForTrainExamplesHistory latest iterations
self.nnet = nn()
def loadTrainExamples(self):
# modelFile = os.path.join(args.examplespath, 'best.pth.tar')
# examplesFile = modelFile+".examples"
examplesFile = os.path.join(args.examplespath, '0.pth.tar_6.examples')
if not os.path.isfile(examplesFile):
print(examplesFile)