def make_training_move(board_state, side):
if cnn_on:
# We must have the first 3x3 board as first 9 entries of the list, second 3x3 board as next 9 entries etc.
# This is required for the CNN. The CNN takes the first 9 entries and forms a 3x3 board etc.
"""If the 10 split 3x3 boards are desired, use create_3x3_board_states(board_state) here"""
np_board_state = create_3x3_board_states(board_state)
else:
np_board_state = np.array(board_state)
np_board_state[np_board_state > 1] = 0
mini_batch_board_states.append(
np_board_state * side
) # append all states are used in the minibatch (+ and - determine which player's state it was)
rand_numb = random.uniform(0., 1.)
if rand_numb < eps:
move = get_random_network_move(board_state, game_spec)
elif deterministic:
move = get_deterministic_network_move(session,
input_layer,
output_layer,
board_state,
side,
valid_only=True,
game_spec=game_spec,
cnn_on=cnn_on)
else:
if mcts:
_, move = monte_carlo_tree_search(game_spec, board_state,
side, 27, session,
input_layer,
output_layer, True,
cnn_on, True)
else:
move = get_stochastic_network_move(session,
input_layer,
output_layer,
board_state,
side,
valid_only=True,
game_spec=game_spec,
cnn_on=cnn_on)
move_for_game = np.asarray(
move
) # The move returned to the game is in a different configuration than the CNN learn move
if cnn_on:
# Since the mini batch states is saved the same way it should enter the neural net (the adapted board state),
# the same should happen for the mini batch moves
move = create_3x3_board_states(np.reshape(
move, [9, 9])) # The function requires a 9x9 array
mini_batch_moves.append(move[0:81])
else:
mini_batch_moves.append(move)
return game_spec.flat_move_to_tuple(move_for_game.argmax())
def make_training_move(board_state, side):
if cnn_on:
np_board_state = create_3x3_board_states(board_state)
else:
np_board_state = np.array(board_state)
mini_batch_board_states.append(np_board_state * side)
rand_numb = random.uniform(0., 1.)
if rand_numb < eps:
move = get_random_network_move(board_state, game_spec)
elif deterministic:
move = get_deterministic_network_move(session,
input_layer,
output_layer,
board_state,
side,
valid_only=True,
game_spec=game_spec,
cnn_on=cnn_on)
else:
if mcts:
_, move = monte_carlo_tree_search(game_spec, board_state,
side, 27, session,
input_layer,
output_layer, True,
cnn_on, True)
else:
move = get_stochastic_network_move(session,
input_layer,
output_layer,
board_state,
side,
valid_only=True,
game_spec=game_spec,
cnn_on=cnn_on)
move_for_game = np.asarray(
move
) # The move returned to the game is in a different configuration than the CNN learn move
if cnn_on:
# Since the mini batch states is saved the same way it should enter the neural net (the adapted board state),
# the same should happen for the mini batch moves
move = create_3x3_board_states(np.reshape(
move, [9, 9])) # The function requires a 9x9 array
mini_batch_moves.append(move[0:81])
else:
mini_batch_moves.append(move)
return game_spec.flat_move_to_tuple(move_for_game.argmax())
def player_func(board_state, side):
if mcts:
_, move = monte_carlo_tree_search(game_spec, board_state, side,
27, session, input_layer,
output_layer, True, cnn_on,
True)
else:
move = get_deterministic_network_move(session,
input_layer,
output_layer,
board_state,
side,
valid_only=True,
game_spec=game_spec)
move_for_game = np.asarray(
move
) # The move returned to the game is in a different configuration than the CNN learn move
return game_spec.flat_move_to_tuple(move_for_game.argmax())
def make_move_historical(net, board_state, side):
if mcts:
_, move = monte_carlo_tree_search(game_spec, board_state, side, 27,
session, input_layer,
output_layer, True, cnn_on, True)
else:
# move = get_deterministic_network_move(session, net[0], net[1], board_state, side,
# valid_only = True, game_spec = game_spec, cnn_on = cnn_on)
move = get_stochastic_network_move(session,
net[0],
net[1],
board_state,
side,
valid_only=True,
game_spec=game_spec,
cnn_on=cnn_on)
move_for_game = np.asarray(
move) # move must be an array, mcts doesn't return this
return game_spec.flat_move_to_tuple(move_for_game.argmax())
def test_move(self):
# Test single move of monte carlo tree search algorithm
game_spec = ut.UltimateTicTacToeGameSpec()
# generate board with 10 random moves
random_func = game_spec.get_random_player_func()
board_state = ut._new_board()
side = 1
for _ in range(10):
move = random_func(board_state, side)
board_state = game_spec.apply_move(board_state, move, side)
side = -1 * side
print("")
ut.print_board_state(board_state, side)
result, move = mc._monte_carlo_sample(game_spec, board_state, side)
print("result: ", result)
print("move: ", move)
mc_func = game_spec.get_monte_carlo_player_func()
result, move = mc.monte_carlo_tree_search(game_spec, board_state, side,
100)
print(result)
print(move)
def monte_carlo_player(self, board_state, side, uct, number_of_samples):
if uct:
_, move = mc.monte_carlo_tree_search_uct(self, board_state, side, number_of_samples)
else:
_, move = mc.monte_carlo_tree_search(self, board_state, side, number_of_samples)
return move