def test_get_q_values_initial_o_turn():
b = np.array([[1, 0, -1],
[1, 0, -1],
[1, 0, 0]]).flatten()
q_table = QTable()
q_values = q_table.get_q_values(Board(b))
expected_q_values = {1: INITIAL_Q_VALUES_FOR_O, 4: INITIAL_Q_VALUES_FOR_O,
7: INITIAL_Q_VALUES_FOR_O, 8: INITIAL_Q_VALUES_FOR_O}
assert q_values == expected_q_values
def test_update_q_value():
qtable = QTable()
b_2d = np.array([[1.0, 0.0, 0.0],
[1.0, -1.0, 0.0],
[0.0, 1.0, -1.0]])
b = b_2d.flatten()
board = Board(b)
qvalues = qtable.get_q_values(board)
init = INITIAL_Q_VALUES_FOR_O
expected_qvalues = {1: init, 2: init, 5: init, 6: init}
assert qvalues == expected_qvalues
b_rot90_flipud_2d = np.flipud(np.rot90(b_2d))
b_rot90_flipud = b_rot90_flipud_2d.flatten()
board_rot90_flipud = Board(b_rot90_flipud)
qtable.update_q_value(board_rot90_flipud, 2, 0.8)
qtable.update_q_value(board_rot90_flipud, 7, 0.7)
assert len(qtable.qtable.cache) == 2
expected_qvalues = {1: init, 2: init, 5: 0.7, 6: 0.8}
qvalues = qtable.get_q_values(board)
assert qvalues == expected_qvalues
expected_qvalues = {2: 0.8, 3: init, 6: init, 7: 0.7}
qvalues = qtable.get_q_values(board_rot90_flipud)
assert qvalues == expected_qvalues
def test_play_training_game_o_player():
q_table = QTable()
move_history = deque()
q_table_player = CELL_O
x_strategy = play_random_move
o_strategy = create_training_player([q_table], move_history, 0)
play_training_game([q_table], move_history, q_table_player, x_strategy,
o_strategy, 0.9, 1)
init = INITIAL_Q_VALUES_FOR_O
first_board = np.copy(new_board)
first_board[6] = CELL_X
val = 0.9 * 0.81
expected_move_indexes_and_q_values = {0: val, 1: init, 2: init,
3: init, 4: init, 5: init,
7: init, 8: val}
move_indexes_and_q_values = q_table.get_q_values(Board(first_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
second_board = np.copy(first_board)
second_board[0] = CELL_O
second_board[8] = CELL_X
val = 0.9 * 0.9
expected_move_indexes_and_q_values = {1: val, 2: init,
3: init, 4: init, 5: init,
7: init}
move_indexes_and_q_values = q_table.get_q_values(Board(second_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
third_board = np.copy(second_board)
third_board[1] = CELL_O
third_board[5] = CELL_X
val = 0.9 * 1.0
expected_move_indexes_and_q_values = {2: val,
3: init, 4: init,
7: init}
move_indexes_and_q_values = q_table.get_q_values(Board(third_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
move_history = deque()
o_strategy = create_training_player([q_table], move_history, 0)
play_training_game([q_table], move_history, q_table_player, x_strategy,
o_strategy, 0.9, 1)
init = INITIAL_Q_VALUES_FOR_O
first_board = np.copy(new_board)
first_board[0] = CELL_X
val = (1 - 0.9) * (0.9 * 0.81) + (0.9 * 0.0)
expected_move_indexes_and_q_values = {1: init, 2: val,
3: init, 4: init, 5: init,
6: val, 7: init, 8: init}
move_indexes_and_q_values = q_table.get_q_values(Board(first_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
second_board = np.copy(first_board)
second_board[2] = CELL_O
second_board[4] = CELL_X
val = 0.9 * -1
expected_move_indexes_and_q_values = {1: val,
3: init, 5: init,
6: init, 7: init, 8: init}
move_indexes_and_q_values = q_table.get_q_values(Board(second_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
def test_play_training_game_x_player():
q_table = QTable()
move_history = deque()
q_table_player = CELL_X
x_strategy = create_training_player([q_table], move_history, 0)
o_strategy = play_random_move
play_training_game([q_table], move_history, q_table_player, x_strategy,
o_strategy, 0.9, 1)
init = INITIAL_Q_VALUES_FOR_X
first_board = np.copy(new_board)
val = 0.9 * 0.81
expected_move_indexes_and_q_values = {0: val, 1: init, 2: val,
3: init, 4: init, 5: init,
6: val, 7: init, 8: val}
move_indexes_and_q_values = q_table.get_q_values(Board(first_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
second_board = np.copy(first_board)
second_board[0] = CELL_X
second_board[7] = CELL_O
val = 0.9 * 0.9
expected_move_indexes_and_q_values = {1: val, 2: init,
3: init, 4: init, 5: init,
6: init, 8: init}
move_indexes_and_q_values = q_table.get_q_values(Board(second_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
third_board = np.copy(second_board)
third_board[1] = CELL_X
third_board[5] = CELL_O
val = 0.9 * 1.0
expected_move_indexes_and_q_values = {2: val,
3: init, 4: init,
6: init, 8: init}
move_indexes_and_q_values = q_table.get_q_values(Board(third_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
move_history = deque()
x_strategy = create_training_player([q_table], move_history, 0)
play_training_game([q_table], move_history, q_table_player, x_strategy,
o_strategy, 0.9, 1)
init = INITIAL_Q_VALUES_FOR_X
first_board = np.copy(new_board)
val = 0.1 * (0.81 * 0.9) + 0.9 * (0.9 * (0.9 * 0.81))
expected_move_indexes_and_q_values = {0: val, 1: init, 2: val,
3: init, 4: init, 5: init,
6: val, 7: init, 8: val}
move_indexes_and_q_values = q_table.get_q_values(Board(first_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
second_board = np.copy(first_board)
second_board[0] = CELL_X
second_board[1] = CELL_O
val = 0.9 * (0.9 * 0.81)
expected_move_indexes_and_q_values = {2: val,
3: init, 4: init, 5: init,
6: init, 7: init, 8: init}
move_indexes_and_q_values = q_table.get_q_values(Board(second_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
third_board = np.copy(second_board)
third_board[2] = CELL_X
third_board[5] = CELL_O
val = 0.9 * 0.81
expected_move_indexes_and_q_values = {3: val, 4: init,
6: init, 7: init, 8: init}
move_indexes_and_q_values = q_table.get_q_values(Board(third_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
fourth_board = np.copy(third_board)
fourth_board[3] = CELL_X
fourth_board[8] = CELL_O
val = 0.81
expected_move_indexes_and_q_values = {4: val,
6: init, 7: init}
move_indexes_and_q_values = q_table.get_q_values(Board(fourth_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values
fifth_board = np.copy(fourth_board)
fifth_board[4] = CELL_X
fifth_board[7] = CELL_O
val = 0.9
expected_move_indexes_and_q_values = {6: val}
move_indexes_and_q_values = q_table.get_q_values(Board(fifth_board))
assert move_indexes_and_q_values == expected_move_indexes_and_q_values