use_random = random.random() < chance

if use_random:

prediction = torch.rand(7)

return prediction

else:

# player foo and bar locked in eternal battle

foo_moves = []

bar_moves = []

foo_preds = []

bar_preds = []

foo_boards = []

bar_boards = []

foos_turn = True

game = connect_four.Game()

# move

while not game.won():

board = game.to_tensor(foos_turn)

if foos_turn:

prediction = request_move(net_one, board)

else:

prediction = request_move(net_two, board)

max_value = torch.max(prediction)

move = np.zeros((1, 7))

for i, element in enumerate(prediction):

if element == max_value:

move[:, i] = 1

prediction[i] = 0.

column = move.tolist()[0].index(1.)

for iter in range(7):

try:

game.insert(column, RED if foos_turn else YELLOW)

break

except Exception as e:

column = (column + 1) % 7

else:

# game.print_board()

return

# switch turn

if foos_turn:

foo_moves.append(move)

foo_preds.append(prediction)

foo_boards.append(board)

foos_turn = False

else:

bar_moves.append(move)

bar_preds.append(prediction)

bar_boards.append(board)

foos_turn = True

game.print_board()

# if its no longer foos turn, he won

good_moves = foo_moves if not foos_turn else bar_moves

good_preds = foo_preds if not foos_turn else bar_preds

good_boards = foo_boards if not foos_turn else bar_boards

bad_moves = foo_moves if foos_turn else bar_moves

bad_moves = [(move - 1) * -1 for move in bad_moves]

bad_preds = foo_preds if foos_turn else bar_preds

bad_boards = foo_boards if foos_turn else bar_boards

# send moves and predictions to update the model

if not foos_turn:

update_model(net_one, good_boards, good_moves, good_preds)

update_model(net_two, bad_boards, bad_moves, bad_preds)

else:

update_model(net_two, good_boards, good_moves, good_preds)