clear = True
propnets = dict()
expertModels = dict()
gameData = dict()
players = dict()
totalRounds = 1601
rotateEvery = 50
replay_buffer = dict()
for game in expertGames:
replay_buffer[game] = pickle.load(open( "buffers/" + game + "2500for400rounds.p", "rb" ) )
data, propnet = load_propnet(to_game)
mnModel = Model(propnet, multiNet=True, games=expertGames, replay_buffer=replay_buffer)
if clear:
print("CLEARING")
mnModel.clear_output_layer()
## train
cur = [None]
mnModel.save(to_game,0,multiNet=True)
start = time.time()
gameIndex = 0
for i in range(totalRounds+1):
if i % rotateEvery == 0:
gameIndex = (gameIndex + 1) % len(expertGames)
#!/usr/bin/env python3.7
import sys
sys.path.insert(1, '/Users/Cameron/Desktop/transfer_ggp')
from model import Model
from mcts import MCTSNode, simulation
from propnet.propnet import load_propnet
import time
start = time.time()
# propnet = load_propnet('connect4match1')
# propnet = load_propnet('tictactoe1')
data, propnet = load_propnet('connectFour')
root = MCTSNode(propnet, data)
# exit(0)
for i in range(400):
simulation(root)
root.print_node()
print('Took', time.time() - start, 'seconds')
from model import Model
from utils.pauser import set_pauser
import time
import sys
import tensorflow as tf
import re
import pickle
if len(sys.argv) < 4:
print("Usage: transfer.py <game> <ckpt> <total_rounds>")
game = sys.argv[1]
expertckpt = sys.argv[2]
totalRounds = int(sys.argv[3])
data, propnet = load_propnet(game)
m = Model(propnet, create=False)
m.load('./models/' + game + '/step-%06d.ckpt' % int(expertckpt))
cur = [None]
start = time.time()
gameIndex = 0
for i in range(totalRounds + 1):
cur[0] = B1Node(propnet, data, model=m)
print('Game number', i)
start_game = time.time()
do_game(cur, propnet, m, z=0.5)
print("took ", time.time() - start_game, "seconds to play game")
pickle.dump(
role_a = ('black',)
role_b = ('white',)
if 'breakthrough-6x6' in from_game.lower():
bd = [5650, 2825, 1412, 706, 353, 176, 88, 50]
rd = []
elif 'breakthroughSmall' in from_game.lower():
bd = [2070, 1035, 517, 258, 129, 64, 50]
rd = []
elif 'connect' in from_game.lower():
role_a = ('red',)
role_b = ('white',)
bd = [685, 342, 171, 85, 50]
rd = []
## load propnet for new game
oldData, old_prop = load_propnet(from_game)
## load propnet for old game
data, new_prop = load_propnet(to_game)
## check dimensions of the input and output layers
oldPropNumActions = {role: len(actions) for role, actions in old_prop.legal_for.items()}
newPropNumActions = {role: len(actions) for role, actions in new_prop.legal_for.items()}
if (len(old_prop.roles) == len(new_prop.roles) and
oldPropNumActions == newPropNumActions and
len(old_prop.propositions) == len(new_prop.propositions)):
model = Model(new_prop, create=False)
model.load('./models/' + from_game + '/step-%06d.ckpt' % int(ckpt))