def __init__(self,size=3): self._t = TTT(size) self._mode = 'optimal' self._penalty_prob = 0 pass
def test_as_second_mover(self): parameters = { "ep_train": 0.5, "ep_infer": 0, "gamma": 1, "alpha": 1, "agent_for": 'minimizer', } q = TabularQ(3) q.set_params(**parameters) opponent_agent = load('minimax') q.train(numOfGames=500, opponent_agent=opponent_agent) t = TTT(3) Q = q._Q updated_state_indices = np.where( Q != [0, 0, 0, 0, 0, 0, 0, 0, 0])[0] # [0] for row indices updated_state_indices = set(updated_state_indices) for i in updated_state_indices: state = q.get_state(i) mover = t.get_mover(state=state) self.assertEqual(mover, -1) return
def minimax(state, mover: int, t: TTT) -> [Score, Move]: next_mover = -1 if mover is 1 else 1 possible_moves = t.get_available_positions(state) corresponding_scores = [] best_score = 0 best_move = None for index in possible_moves: next_state = state.copy() next_state[index] = mover if t.is_terminated(next_state): score = t.get_score(next_state) corresponding_scores.append(score) else: [score, _] = minimax(next_state, next_mover, t) corresponding_scores.append(score) if mover == 1: best_score = max(corresponding_scores) best_move_index = corresponding_scores.index(best_score) best_move = possible_moves[best_move_index] elif mover == -1: best_score = min(corresponding_scores) best_move_index = corresponding_scores.index(best_score) best_move = possible_moves[best_move_index] return [best_score, best_move]
def minimax_save(state, mover: int, t: TTT, table) -> (Score, Move): encoded_state = encode_state(state) if encode_state in table: return table[encoded_state] next_mover = -1 if mover is 1 else 1 possible_moves = t.get_available_positions(state) corresponding_scores = [] best_score = 0 best_move = None for index in possible_moves: next_state = state.copy() next_state[index] = mover if t.is_terminated(next_state): score = t.get_score(next_state) corresponding_scores.append(score) else: [score, _] = minimax_save(next_state, next_mover, t, table) corresponding_scores.append(score) if mover == 1: best_score = max(corresponding_scores) best_move_index = corresponding_scores.index(best_score) best_move = possible_moves[best_move_index] elif mover == -1: best_score = min(corresponding_scores) best_move_index = corresponding_scores.index(best_score) best_move = possible_moves[best_move_index] table[encoded_state] = (best_score, best_move) return (best_score, best_move)
def test_row(self): t3 = TTT(3) s3 = np.array([0, 0, 0, -1, -1, 0, 1, 1, 1]) self.assertEqual(t3.check_winner(s3), { 'winner': 1, 'lines': [[6, 7, 8]] }) self.assertTrue(t3.is_terminated(s3))
def test_column(self): t4 = TTT(4) s4 = np.array([1, 0, 0, -1, 0, 1, 0, -1, 1, 0, 0, -1, 0, 1, 0, -1]) self.assertEqual(t4.check_winner(s4), { 'winner': -1, 'lines': [[3, 7, 11, 15]] }) self.assertTrue(t4.is_terminated(s4))
def test_score1(self): t3 = TTT(3) s = [[1, -1, 0], [-1, 1, 0], [0, 0, 1]] s = np.array(s).reshape(-1) terminated = t3.is_terminated(s) score = t3.get_score(s) self.assertTrue(terminated) self.assertEqual(score, 5)
def test_get_mover(self): t = TTT(3) s = [[0, 1, 0], [0, -1, 0], [0, 0, 0]] s = np.array(s) s = s.reshape(-1) mover = t.get_mover(state=s) self.assertTrue(mover == 1)
def initialize_minimax(filepath: str, size=3): table = {} t = TTT(size) minimax_save(t.get_state(), t.get_mover(), t, table) with open(filepath, 'wb') as f: pickle.dump(table, f) return
def test_minimax_1(self): t = TTT(3) state = [[1, -1, 0], [-1, 1, 0], [0, 0, 0]] state = np.array(state, dtype='int') state = state.reshape(-1) t._state = state [score, move] = minimax(t.get_state(), 1, t) self.assertListEqual(list(state), list(t.get_state())) self.assertEqual(8, move) self.assertEqual(5, score)
def test_alpha_beta_1(self): t = TTT(3) player = ABPruning(3) state = [[1, -1, 0], [-1, 1, 0], [0, 0, 0]] state = np.array(state, dtype='int') state = state.reshape(-1) t._state = state [score, move] = player.get(t.get_state(), 1) self.assertListEqual(list(state), list(t.get_state())) self.assertEqual(8, move) self.assertEqual(5, score)
def test_result1(self): t3 = TTT(3) s = [[1, -1, -1], [-1, 1, 1], [1, -1, 1]] s = np.array(s).reshape(-1) result = t3.get_result(s) to_equal = { 'terminated': True, 'score': 1, 'winner': 1, 'lines': [[0, 4, 8]] } self.assertDictEqual(result, to_equal)
def test_set_state(self): t = TTT(3) state = [1, 0, 0, 1, 1, 0, -1, -1, 0] t.set_state(state) mover = t.get_mover() order = t._order num_of_moves = t._num_moves _state = np.array(state, dtype=int) self.assertEqual(mover, -1) self.assertEqual(order, False) self.assertEqual(num_of_moves, 5) self.assertTrue(np.array_equal(_state, t._state))
def initialize_state_indices(filepath: str, size=3): table = {'current': 0} # store state:index pair t = TTT(size) def dfs(state, mover: int, table=table) -> None: # store if the state is new one : encoded_state = t.get_encoded_state(state) if not encoded_state in table: table[encoded_state] = table['current'] table['current'] += 1 assert type(table[encoded_state]) is int next_mover = 1 if mover is -1 else -1 available_moves = t.get_available_positions(state) for i in available_moves: next_state = state.copy() next_state[i] = mover if not t.is_terminated(next_state): dfs(next_state, next_mover) return # indexing start : initial_mover = t.get_mover() initial_state = t.get_state() print('indexing start :') dfs(initial_state, initial_mover) # simple validate : num_visited = table['current'] del (table['current']) num_stored = len(table) print(f'visited states : {num_visited}') print(f'stored states : {num_stored}') assert num_stored == num_visited indices = set(table.values()) assert len(indices) == len(table) sample_index = list(table.values())[1] assert type(sample_index) is int # save : print('saving... ', end='') with open(filepath, 'wb') as f: pickle.dump(table, f) print('done') return
def test_update(self): t = TTT(3) prev_state = [[1, 1, 0], [-1, -1, 0], [0, 0, 0]] next_state = [[1, 1, 1], [-1, -1, 0], [0, 0, 0]] prev_state = np.array(prev_state).reshape(-1) next_state = np.array(next_state).reshape(-1) result = t.get_result(next_state) self.assertEqual(result, {'terminated': True, 'score': 5}) q = TabularQ(3) q.set_params(alpha=1, gamma=1) encoded_prev_state = t.get_encoded_state(prev_state) prev_state_index = q.get_index(encoded_prev_state) encoded_next_state = t.get_encoded_state(next_state) next_state_index = q.get_index(encoded_next_state) self.assertEqual(next_state_index, None) q.update(encoded_prev_state, 2, encoded_next_state, 5) updated_row = q._Q[prev_state_index, :] check_row = np.array_equal(updated_row, [0, 0, 5, 0, 0, 0, 0, 0, 0]) self.assertTrue(check_row) # test correct inference : q._is_first_mover = True possible_moves = t.get_available_positions(prev_state) inferred = q.infer(encoded_prev_state, possible_moves, 1) self.assertEqual(inferred, 2) pass
def __init__(self, user_first: bool, size=3, *args, **kwargs): super().__init__(*args, **kwargs) # state variables self._user_first = user_first self._t = TTT(size) self._agent: Callable[[np.ndarray], int] self._num_of_moves = 0 self._state_history = [self._t.get_state()] # UI accessors self._history_scale: tk.Scale self._player_labels: Dict[int, tk.Label] # key : 1,2 self._buttons = [] # UI initialization self.title(f'TTT') self._make_top_frame() self._make_board(size) self._make_bottom_frame(size) return
def run_game(self, agent1, agent2, size=3): t = TTT(size) for i in range(size * size): agent = agent1 if i % 2 == 0 else agent2 inferred = agent(t.get_state()) t.put(inferred) if t.is_terminated(): break return t.get_result()
def test_diagonal2(self): t3 = TTT(3) s3 = np.array([1, 0, -1, 1, -1, 0, -1, 1, 0]) self.assertEqual(t3.check_winner(s3), { 'winner': -1, 'lines': [[2, 4, 6]] }) self.assertTrue(t3.is_terminated(s3)) t4 = TTT(4) s4 = np.array([-1, 0, 0, 1, -1, 0, 1, 0, -1, 1, 0, 0, 1, 0, 0, 0]) self.assertTrue(t4.is_terminated(s4)) self.assertEqual(t4.check_winner(s4), { 'winner': 1, 'lines': [[3, 6, 9, 12]] })
def test_diagonal1(self): t3 = TTT(3) s3 = np.array([1, 0, -1, 0, 1, -1, 0, 0, 1]) self.assertTrue(t3.is_terminated(s3)) self.assertEqual(t3.check_winner(s3), { 'winner': 1, 'lines': [[0, 4, 8]] }) t4 = TTT(4) s4 = np.array([-1, 0, 0, 1, 0, -1, 0, 1, 0, 1, -1, 0, 1, 0, 0, -1]) self.assertTrue(t4.is_terminated(s4)) self.assertEqual(t4.check_winner(s4), { 'winner': -1, 'lines': [[0, 5, 10, 15]] })
def test_alphabeta_vs_alphabeta(self): t = TTT(3) player = ABPruning(3) moves = 0 print('Moves : 0 ', end='') while True: [_, best_move] = player.get(t.get_state(), t.get_mover()) t.put(best_move) moves += 1 print(f'{moves} ', end='') if t.is_terminated(): break pass print('final state') print(t) self.assertEqual(t.check_winner()['winner'], 0)
def test_minimax_vs_minimax(self): size = 3 t = TTT(size) filepath = 'results/minimax.pk' minimax_loaded = minimax_load(filepath) moves = 0 while True: [_, best_move] = minimax_loaded(t.get_state()) t.put(best_move) moves += 1 if t.is_terminated(): break pass self.assertEqual(t.check_winner()['winner'], 0) pass
def _train_both(self,numOfGames): for _ in tqdm(range(numOfGames)): game = TTT(self._size) self._is_first_mover = True # one complete game : while True: encoded_prev_state = game.get_encoded_state() possible_moves = game.get_available_positions() selected_move = self._epsilon_greedy_train(encoded_prev_state,possible_moves) game.put(selected_move) encoded_next_state = game.get_encoded_state() result = game.get_result() self.update(encoded_prev_state,selected_move,encoded_next_state,result['score']) if result['terminated']: break pass pass
def test_deterministic_vs_minimax(self): # gamma, alpha == 1 guarantees that for endstates s and optimal move a, # Q(s,a) = R(s,a) IF Q(s,a) IS NOT 0 # Here, R(s,a) is the score of the terminated state parameters = { "ep_train": 0.5, "ep_infer": 0, "gamma": 1, "alpha": 1, "agent_for": 'both', } q = TabularQ(3) q.set_params(**parameters) q.train(numOfGames=500) s = Settings() minimax = minimax_load(s.path('minimax')) t = TTT(3) Q = q._Q to_check_state_indices = np.where(Q != [0, 0, 0, 0, 0, 0, 0, 0, 0])[0] to_check_state_indices = map(int, to_check_state_indices) for state_index in to_check_state_indices: self.assertFalse( np.array_equal(Q[state_index], np.array([0, 0, 0, 0, 0, 0, 0, 0, 0]))) state = q.get_state(state_index) encoded_state = t.get_encoded_state(state) mover = t.get_mover(state=state) possible_moves = t.get_available_positions(state) if mover == 1: best_move_q = np.argmax(Q[state_index]) if int(Q[state_index, best_move_q]) is not 0: move_inferred = q.infer(encoded_state, possible_moves, mover) q_value_1 = Q[state_index, best_move_q] q_value_2 = Q[state_index, move_inferred] self.assertEqual(q_value_1, q_value_2) elif mover == -1: best_move_q = np.argmin(Q[state_index]) if int(Q[state_index, best_move_q]) is not 0: move_inferred = q.infer(encoded_state, possible_moves, mover) q_value_1 = Q[state_index, best_move_q] q_value_2 = Q[state_index, move_inferred] self.assertEqual(q_value_1, q_value_2) next_state = state.copy() next_state[best_move_q] = mover result = t.get_result(next_state) if result['terminated']: best_score, _ = minimax(state) q_value = Q[state_index, best_move_q] if best_score != q_value: # not yet sampled (s,a) # or withdraw case self.assertEqual(q_value, 0) else: # sampled (s,a) self.assertEqual(best_score, q_value) pass
class GameWindow(tk.Toplevel): """Game UI""" def __init__(self, user_first: bool, size=3, *args, **kwargs): super().__init__(*args, **kwargs) # state variables self._user_first = user_first self._t = TTT(size) self._agent: Callable[[np.ndarray], int] self._num_of_moves = 0 self._state_history = [self._t.get_state()] # UI accessors self._history_scale: tk.Scale self._player_labels: Dict[int, tk.Label] # key : 1,2 self._buttons = [] # UI initialization self.title(f'TTT') self._make_top_frame() self._make_board(size) self._make_bottom_frame(size) return #region Public Methods def set_agent(self, agent: Callable[[np.ndarray], int], name: str) -> None: self._agent = agent return def get_result(self) -> dict: return self._t.get_result() #endregion #region Put UI Components def _make_top_frame(self): frame = tk.Frame(self) if self._user_first: text1 = 'O : User' text2 = 'X : AI' else: text1 = 'O : AI' text2 = 'X : User' label1 = tk.Label(frame, text=text1) label2 = tk.Label(frame, text=text2) label1.pack() label2.pack() frame.pack() return def _make_board(self, size): board = tk.Frame(self) buttons = self._buttons num_of_buttons = size * size for i in range(num_of_buttons): b = tk.Button(board, width=3, height=1, font=('Helvetica', 30), activebackground='white', command=lambda num=i: self._on_click_board(num)) buttons.append(b) b.grid(column=i % size, row=int(i / size)) pass board.pack() return def _make_bottom_frame(self, size): frame = tk.Frame(self) history_scale = tk.Scale(frame, command=self._on_scale_move, orient='horizontal', from_=0, to=0) history_scale.grid(row=0, columnspan=2) self._history_scale = history_scale restart_button = tk.Button(frame, text="Restart", command=self._on_click_reset) exit_button = tk.Button(frame, text="Exit", command=self.destroy) restart_button.grid(row=1, column=0) exit_button.grid(row=1, column=1) frame.pack() return #endregion #region Event Handlers def _on_click_board(self, position: int): state_num = int(self._history_scale.get()) is_rewinded = not (self._num_of_moves == state_num) if is_rewinded: # reset the game to the rewinded one : state_to_force = self._state_history[state_num] self._t.set_state(state_to_force) self._num_of_moves = self._t._num_moves self._state_history = self._state_history[0:(self._num_of_moves + 1)] pass self._t.put(position) current_state = self._t.get_state() self._state_history.append(current_state) self._num_of_moves += 1 self._history_scale.configure(to=self._num_of_moves) self._history_scale.set(self._num_of_moves) """ [issue] If this procedure is called by button.invoke() then it doesn't invoke the scale's command _on_scale_move. So call it manually (and hence, called twice in user's turn) : """ self._on_scale_move(self._num_of_moves) return def _on_scale_move(self, state_num): state_num = int(state_num) first_mover_turn = True if state_num % 2 == 0 else False user_turn = first_mover_turn == self._user_first self._set_board(state_num, user_turn) if self.get_result()['terminated']: return if state_num == len(self._state_history) - 1: if user_turn: pass else: if hasattr(self, '_agent'): self._on_agent_turn(state_num) pass else: # : agent's turn but it's a previous state pass return def _on_click_reset(self): self._num_of_moves = 0 self._state_history = self._state_history[0:1] self._t.set_state(self._state_history[0]) self._history_scale.configure(to=0) self._history_scale.set(0) self._set_board(0, self._user_first == True) return #endregion #region Private Methods def _on_agent_turn(self, state_num: int): # TODO : async progress bar state = self._state_history[state_num] move = self._agent(state) button = self._buttons[move] button.configure(state='normal') button.invoke() return def _set_board(self, state_num: int, user_turn: bool): """Modify board UI""" to_state = self._state_history[state_num] result = self._t.get_result(to_state) terminated = result['terminated'] lines = result['lines'] lines = sum(lines, []) # flattening for p in range(len(to_state)): move = int(to_state[p]) of_line = p in lines self._modify_button(p, move, user_turn, terminated, of_line) return def _modify_button(self, button_position: int, mover: int, move_allowed: bool, terminated=False, of_line=False): button = self._buttons[button_position] args = {'disabledforeground': 'black', 'state': 'disabled'} if mover == 1: args['text'] = '○' args['state'] = 'disabled' elif mover == -1: args['text'] = '×' args['state'] = 'disabled' else: args['text'] = ' ' if move_allowed: args['state'] = 'normal' elif not hasattr(self, '_agent'): args['state'] = 'normal' if terminated: args['state'] = 'disabled' if of_line: if mover == 1: args['disabledforeground'] = 'steelblue' elif mover == -1: args['disabledforeground'] = 'tomato' button.config(**args) return
def _train_against(self,opponent_agent:Callable[[np.ndarray],int],numOfGames:int)->None: agent_q_turn = self._is_first_mover for _ in tqdm(range(numOfGames)): game = TTT(self._size) turn = True # one complete game : # prev state, action taken are from agent's turn # next state is from opponent's turn. # update in opponent's turn encoded_prev_state = None move_taken = None encoded_next_state = None while True: if turn is agent_q_turn: # Q turn : if game.is_terminated(): break else: possible_moves = game.get_available_positions() encoded_prev_state = game.get_encoded_state() move_taken = self._epsilon_greedy_train(encoded_prev_state,possible_moves) game.put(move_taken) pass pass else: # opponent's turn : if not game.is_terminated(): state = game.get_state() # move below is considered as random (sampling procedure) : move = opponent_agent(state) game.put(move) pass encoded_next_state = game.get_encoded_state() score = game.get_score() if encoded_prev_state is not None: # : to avoid just after first move case ( in case of Q is second mover ) self.update(encoded_prev_state,move_taken,encoded_next_state,score) pass turn = not turn pass return None
class ABPruning: def __init__(self,size=3): self._t = TTT(size) self._mode = 'optimal' self._penalty_prob = 0 pass def set_penalty(self,penalty_prob=0): assert type(penalty_prob) == int or type(penalty_prob) == float assert penalty_prob >= 0 assert penalty_prob <= 1 if penalty_prob > 0: self._mode = 'modified' self._penalty_prob = penalty_prob else: pass return def get(self,state:np.ndarray,mover:int)->(Score,Move): if self._mode == 'optimal': return self._optimal(state,mover) elif self._mode == 'modified': return self._modified(state,mover) def _optimal(self,state,mover:int,alpha=-1000,beta=1000)->(Score,Move): t = self._t next_mover = -1 if mover is 1 else 1 possible_moves = t.get_available_positions(state) best_move = None best_score = None # maximizer : if mover == 1: best_score = -1000 for i in possible_moves: next_state = state.copy() next_state[i] = mover if t.is_terminated(next_state): score = t.get_score(next_state) else: [score,_] = self._optimal(next_state,next_mover,alpha,beta) if score > best_score: best_score = score best_move = i alpha = best_score if alpha >= beta: break # minimizer : elif mover == -1: best_score = 1000 for i in possible_moves: next_state = state.copy() next_state[i] = mover if t.is_terminated(next_state): score = t.get_score(next_state) else: [score,_] = self._optimal(next_state,next_mover,alpha,beta) if score < best_score: best_score = score best_move = i beta = best_score if alpha >= beta: break return (best_score, best_move) def _modified(self,state,mover:int,alpha=-1000,beta=1000)->(Score,Move): t = self._t next_mover = -1 if mover is 1 else 1 possible_moves = self._get_reduced_moves(state) best_move = None best_score = None # maximizer : if mover == 1: best_score = -1000 for i in possible_moves: next_state = state.copy() next_state[i] = mover if t.is_terminated(next_state): score = t.get_score(next_state) else: [score,_] = self._modified(next_state,next_mover,alpha,beta) if score > best_score: best_score = score best_move = i alpha = best_score if alpha >= beta: break # minimizer : elif mover == -1: best_score = 1000 for i in possible_moves: next_state = state.copy() next_state[i] = mover if t.is_terminated(next_state): score = t.get_score(next_state) else: [score,_] = self._modified(next_state,next_mover,alpha,beta) if score < best_score: best_score = score best_move = i beta = best_score if alpha >= beta: break return (best_score, best_move) def _get_reduced_moves(self,state)->tuple: all_moves = self._t.get_available_positions(state) if len(all_moves) == 0: return [] p = 1 - self._penalty_prob num_of_moves = int(len(all_moves)*p) if num_of_moves == 0: num_of_moves = 1 sample_moves = random.sample(all_moves,num_of_moves) return sample_moves
def load(name: str, **kwargs) -> Callable[[np.ndarray], int]: if name == 'minimax': from src.utils.path import Settings s = Settings() minimax = minimax_load(s.path('minimax')) def agent(state: np.ndarray) -> int: move = minimax(state)[1] return int(move) return agent elif name == 'alpha_beta': assert 'size' in kwargs assert 'penalty_prob' in kwargs size = kwargs.get('size') penalty_prob = kwargs.get('penalty_prob') ab = ABPruning(size) ab.set_penalty(penalty_prob) t = TTT(size) def agent(state: np.ndarray) -> int: mover = t.get_mover(state=state) inferred = ab.get(state=state, mover=mover)[1] return inferred return agent elif name == 'random': assert 'size' in kwargs import random size = kwargs.get('size') t = TTT(size) def agent(state: np.ndarray) -> int: possible_moves = t.get_available_positions(state) nums = len(possible_moves) random_index = random.randint(0, nums - 1) return int(possible_moves[random_index]) return agent elif name == 'tabular_q': assert 'id' in kwargs id = kwargs.get('id') q = TabularQ.load(id) size = q._size t = TTT(size) def agent(state: np.ndarray) -> int: possible_moves = t.get_available_positions(state) encoded_state = t.get_encoded_state(state) mover = t.get_mover(state=state) inferred = q.infer(encoded_state, possible_moves, mover) return inferred return agent else: raise NameError(f'{name} is not implemented')
def test_state_encode(self): t3 = TTT(3) encoded01 = t3.get_encoded_state() state02 = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int) encoded02 = t3.get_encoded_state(state02) self.assertEqual(encoded01, encoded02)
def test_game1(self): # 0 1 0 # -1 1 0 # -1 1 0 t = TTT(3) result = t.get_result() self.assertDictEqual(result, {'terminated': False, 'score': 0}) t.put(1) result = t.get_result() self.assertDictEqual(result, {'terminated': False, 'score': 0}) t.put(3) result = t.get_result() self.assertDictEqual(result, {'terminated': False, 'score': 0}) t.put(4) result = t.get_result() self.assertDictEqual(result, {'terminated': False, 'score': 0}) t.put(6) result = t.get_result() self.assertDictEqual(result, {'terminated': False, 'score': 0}) t.put(7) result = t.get_result() self.assertDictEqual(result, {'terminated': True, 'score': 5}) return
def test_availables(self): t3 = TTT(3) s3 = [[1, -1, 0], [0, 1, -1], [1, -1, 0]] s3 = np.array(s3).reshape(-1) indices = t3.get_available_positions(s3) self.assertListEqual(indices, [2, 3, 8])