from utils import *
"""
use this script to play any two agents against each other, or play manually with
any agent.
"""
g = OthelloGame(6)
# all players
rp = RandomPlayer(g).play
gp = GreedyOthelloPlayer(g).play
hp = HumanOthelloPlayer(g).play
# nnet players
n1 = NNet(g)
n1.load_checkpoint('./pretrained_models/othello/pytorch/','6x100x25_best.pth.tar')
args1 = dotdict({'numMCTSSims': 50, 'cpuct':1.0})
mcts1 = MCTS(g, n1, args1)
n1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0))
#n2 = NNet(g)
#n2.load_checkpoint('/dev/8x50x25/','best.pth.tar')
#args2 = dotdict({'numMCTSSims': 25, 'cpuct':1.0})
#mcts2 = MCTS(g, n2, args2)
#n2p = lambda x: np.argmax(mcts2.getActionProb(x, temp=0))
arena = Arena.Arena(n1p, hp, g, display=display)
print(arena.playGames(2, verbose=True))
any agent.
"""
with open('othello/book/XOT/openingssmall.txt') as my_file:
book_array = my_file.readlines()
g = OthelloGame(8)
# all players
rp = RandomPlayer(g)
gp = GreedyOthelloPlayer(g)
hp = HumanOthelloPlayer(g)
edax = EdaxPlayer(3, g)
# nnet players
n1 = NNet(g)
n1.load_checkpoint('./temp/', 'restart.pth.tar')
#n1.load_checkpoint('./pretrained_models/othello/pytorch','restart.pth.tar')
args1 = dotdict({'numMCTSSims': 600, 'cpuct': 1.0})
n1p = NeuralPlayer(g, n1, args1)
n2 = NNet(g)
n2.load_checkpoint('./pretrained_models/othello/pytorch',
'8x8_100checkpoints_best.pth.tar')
args2 = dotdict({'numMCTSSims': 200, 'cpuct': 1.0})
n2p = NeuralPlayer(g, n2, args2, False)
arena = Arena.Arena(n1p, edax, g, display=display, book=book_array)
print(arena.playGames(10, verbose=True))
import numpy as np
from utils import *
"""
use this script to play any two agents against each other, or play manually with
any agent.
"""
g = OthelloGame(6)
# all players
rp = RandomPlayer(g).play
gp = GreedyOthelloPlayer(g).play
hp = HumanOthelloPlayer(g).play
# nnet players
n1 = NNet(g)
n1.load_checkpoint('./pretrained_models/', '6x100x25_best.pth.tar')
args1 = dotdict({'numMCTSSims': 50, 'cpuct': 1.0})
mcts1 = MCTS(g, n1, args1)
n1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0))
#n2 = NNet(g)
#n2.load_checkpoint('/dev/8x50x25/','best.pth.tar')
#args2 = dotdict({'numMCTSSims': 25, 'cpuct':1.0})
#mcts2 = MCTS(g, n2, args2)
#n2p = lambda x: np.argmax(mcts2.getActionProb(x, temp=0))
arena = Arena.Arena(n1p, hp, g, display=display)
print(arena.playGames(2, verbose=True))
g = nimGame(config)
# all players
#rp = RandomPlayer(g).play
if gameChoice == 0:
gp = GreedyOthelloPlayer(g).play
hp = HumanOthelloPlayer(g).play
elif gameChoice == 1:
hp = HumanTicTacToePlayer(g).play
elif gameChoice == 2:
hp = HumanNimPlayer(g).play
# nnet players
if gameChoice == 0:
n1 = NNet(g)
else:
n1 = nn(g)
if gameChoice == 0:
if mini_othello:
n1.load_checkpoint('./pretrained_models/othello/pytorch/',
'6x100x25_best.pth.tar')
else:
n1.load_checkpoint('./pretrained_models/othello/pytorch/',
'8x8_100checkpoints_best.pth.tar')
elif gameChoice == 1:
n1.load_checkpoint(
'/Users/mettinger/github/alpha-zero-general/pretrained_models/tictactoe/keras',
'best-25eps-25sim-10epch.pth.tar')
elif gameChoice == 2:
import numpy as np
from utils import *
"""
use this script to play any two agents against each other, or play manually with
any agent.
"""
g = OthelloGame()
# all players
rp = RandomPlayer(g).play
#gp = GreedyOthelloPlayer(g).play
hp = HumanOthelloPlayer(g).play
# nnet players
n1 = NNet(g)
n1.load_checkpoint('./temp/', 'checkpoint_2.pth.tar')
args1 = dotdict({'numMCTSSims': 50, 'cpuct': 1.0})
mcts1 = MCTS(g, n1, args1)
n1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0))
#n2 = NNet(g)
#n2.load_checkpoint('/dev/8x50x25/','best.pth.tar')
#args2 = dotdict({'numMCTSSims': 25, 'cpuct':1.0})
#mcts2 = MCTS(g, n2, args2)
#n2p = lambda x: np.argmax(mcts2.getActionProb(x, temp=0))
arena = Arena.Arena(n1p, hp, g, display=display)
print(arena.playGames(2, verbose=True))
mini_othello = False # Play in 6x6 instead of the normal 8x8.
human_vs_cpu = True
if mini_othello:
g = OthelloGame(6)
else:
g = OthelloGame(8)
# all players
rp = RandomPlayer(g).play
gp = GreedyOthelloPlayer(g).play
hp = HumanOthelloPlayer(g).play
# nnet players
n1 = NNet(g)
if mini_othello:
n1.load_checkpoint('./pretrained_models/othello/pytorch/',
'6x100x25_best.h5')
else:
n1.load_checkpoint('./pretrained_models/othello/pytorch/',
'8x8_100checkpoints_best.h5')
args1 = dotdict({'numMCTSSims': 50, 'cpuct': 1.0})
mcts1 = MCTS(g, n1, args1)
def n1p(x): return np.argmax(mcts1.getActionProb(x, temp=0))
if human_vs_cpu:
player2 = hp
else:
n2 = NNet(g)