def play_arena(model):
game = GobangGame(col=12, row=4, nir=7, defender=-1)
heur = Heuristic(game)
heuristic = Heuristic(game).random_play
model_player = lambda b, p: np.argmax(model.predict(h.b))
arena = Arena.Arena(model_player, heuristic, game, display=display)
return arena.playGames(100, verbose=True)
def __init__(self, model):
self.game = GobangGame(col=12, row=4, nir=7, defender=-1)
self.heuristic = Heuristic(self.game)
heuristic_player = Heuristic(self.game).random_play
model_player = lambda b, p: Model_Arena.model_player(self, b, p, model)
self.arena = Arena.Arena(model_player,
heuristic_player,
self.game,
display=display)
import Arena
from MCTS import MCTS
from gobang.GobangGame import GobangGame, display
from gobang.GobangPlayers import *
from gobang.keras.NNet import NNetWrapper as NNet
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 = GobangGame()
# all players
rp = RandomPlayer(g).play
gp = GreedyGobangPlayer(g).play
hp = HumanGobangPlayer(g).play
# nnet players
n1 = NNet(g)
n1.load_checkpoint('./temp/', '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)
def test_gobang_tensorflow(self):
self.execute_game_test(GobangGame(), GobangTensorflowNNet)
def test_gobang_keras(self):
self.execute_game_test(GobangGame(), GobangKerasNNet)
from utils import *
"""
use this script to play any two agents against each other, or play manually with
any agent.
"""
choice = "othello"
if choice == "tictactoe":
g = TicTacToeGame(3)
n1 = NNet(g)
n1.load_checkpoint('./temp/', 'best75_eps95_dim3.pth.tar')
gamename = "tictactoe"
display = display
hp = HumanTicTacToePlayer(g).play
if choice == "gobang":
g = GobangGame(5, 4)
n1 = NNet1(g)
n1.load_checkpoint('./temp/', 'curent13temp_iter75_eps350_dim5.pth.tar')
gamename = "gobang"
display = display1
hp = HumanGobangPlayer(g).play
if choice == "othello":
g = OthelloGame(8)
n1 = NNet2(g)
n1.load_checkpoint('./temp/', 'curent14temp:iter14:eps200:dim8.pth.tar')
gamename = "othello"
display = display2
hp = MinMaxOthelloPlayer(g, 3).play
if choice == "connect4":
g = Connect4Game(5, 6)
n1 = NNet3(g)
import numpy as np
from utils import *
"""
use this script to play any two agents against each other, or play manually with
any agent.
"""
choice = "othello"
if choice == "tictactoe":
g = TicTacToeGame(5)
n1 = NNet(g)
n1.load_checkpoint('./temp/', 'best75_eps95_dim5.pth.tar')
display = display
hp = MinMaxTicTacToePlayer(g, 4).play
if choice == "gobang":
g = GobangGame(6, 6)
n1 = NNet1(g)
n1.load_checkpoint('./temp/', 'temp:iter75:eps5:dim6.pth.tar')
display = display1
hp = MinMaxGobangPlayer(g, 6).play
if choice == "othello":
g = OthelloGame(6)
n1 = NNet2(g)
n1.load_checkpoint('./temp/', 'best75:eps140:dim6.pth.tar')
display = display2
hp = MinMaxOthelloPlayer(g, 4).play
if choice == "connect4":
g = Connect4Game(6, 7)
n1 = NNet3(g)
n1.load_checkpoint('./temp/', 'best75:eps1:dim6.pth.tar')
display = display3
pool.close()
pool.join()
oneWon = 0
twoWon = 0
draws = 0
for i in res:
result.append(i.get())
for i in result:
oneWon += i[0]
twoWon += i[1]
draws += i[2]
print("Model 1 Win:",oneWon," Model 2 Win:",twoWon," Draw:",draws)
g = GobangGame(col=12, row=4, nir=7, defender=-1)
os.environ["CUDA_VISIBLE_DEVICES"] = modeargs.gpu
args1 = dotdict({'numMCTSSims': 1000, 'cpuct':1.0, 'evaluationDepth':1, 'multiGPU': True,
'setGPU':'0,1','alpha':0.3,'epsilon':0.25,'fast_eval':True,
'numSelfPlayProcess': 10,'numPerProcessSelfPlay': 150,})
# all players
rp = RandomPlayer(g).play
hp = HumanGobangPlayer(g).play
heuristic_rand = Heuristic(g).random_play
heuristic = Heuristic(g).play
if modeargs.mode == 'learn':
#arena = Arena.Arena(n1p, heuristic_rand, g, display=display, mcts=mcts1)
#print(arena.playGames(50, verbose=True))
parallel_play_arena(args1, g, heuristic_rand)
print("Well Done")
any agent.
Author: Gideon Hanse, Dyon van Vreumingen
"""
import numpy as np
import py_compile as cmp
from utils import *
from Arena import *
from gobang.GobangGame import GobangGame, display
from gobang.GobangPlayers import *
from MCS import MCSPlayer
from Qlearning import QlearningPlayer
#from MCTS import MCTSPlayer
cmp.compile("gobang/GobangPlayers.py")
g = GobangGame(9)
# p1 = MCSPlayer(g)
p1 = QlearningPlayer(g)
p1.train(10000)
p2 = RandomPlayer(g)
print("Gobang")
arena = Arena(p1.play, p2.play, g, display=display)
print(arena.playGames(1000, verbose=False))