def test_against_random(model, size=100):
'''
Evaluate the model against random performance
'''
wins = {0: 0, 1: 0, 2: 0}
for _ in range(size):
game = TicTacToe(3)
pid = np.random.random_integers(low=1, high=2, size=1)[0]
winner = None
while winner is None:
board = game.get_board(pid)
if pid == 1:
x, y = random_choice(board)
else:
r_board = game.get_board_raw()
x, y, _ = evaluate(model, game, pid, tau=1)
game.place(pid, x, y)
winner = game.check_win()
pid = (pid % 2) + 1
wins[winner] += 1
print('Wins: %d Ties: %d Losses: %d' % (wins[2], wins[0], wins[1]))
return (wins[2] / (wins[0] + wins[1] + wins[2]))
for it in range(ITER):
for i in range(EPOCH):
print('{}: {}/{}'.format(it, i, EPOCH), end='\r')
game = TicTacToe(3)
pid = np.random.random_integers(low=1, high=2, size=1)[0]
winner = None
inputs = {1: [], 2: []}
probs = {1 : [], 2: []}
while winner is None:
board = game.get_board(pid)
r_board = game.get_board_raw()
x, y, prob = evaluate(model, game, (pid % 2) + 1)
inputs[pid].append(copy.copy(game.get_input(pid)))
probs[pid].append(prob)
game.place(pid, x, y)
winner = game.check_win()
pid = (pid % 2) + 1
if winner != 0:
one_reward = [1 if winner == 1 else -1] * len(inputs[1])
two_reward = [1 if winner == 2 else -1] * len(inputs[2])
else:
one_reward = [0] * len(inputs[1])
