tesauro fat and diffs in values
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c31bc39780
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60
board.py
60
board.py
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@ -51,7 +51,6 @@ class Board:
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# board += ([1, 0] if np.sign(player) > 0 else [0, 1])
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# return np.array(board).reshape(1,30)
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# quack-fatter
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@staticmethod
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def board_features_quack_norm(board, player):
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@ -66,7 +65,7 @@ class Board:
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board.append(15 - sum(positives))
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board.append(-15 - sum(negatives))
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board += ([1, 0] if np.sign(player) > 0 else [0, 1])
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return np.array(board).reshape(1,30)
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return np.array(board).reshape(1, 30)
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# tesauro
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@staticmethod
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@ -95,9 +94,62 @@ class Board:
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board_rep += bar_trans(board, player)
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board_rep += (15 - Board.num_of_checkers_for_player(board, player),)
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board_rep += ([1,0] if cur_player == 1 else [1,0])
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board_rep += ([1, 0] if cur_player == 1 else [0, 1])
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return np.array(board_rep).reshape(1,198)
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return np.array(board_rep).reshape(1, 198)
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@staticmethod
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def board_features_tesauro_fat(board, cur_player):
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def ordinary_trans(val, player):
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abs_val = val*player
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if abs_val <= 0:
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return (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
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elif abs_val == 1:
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return (1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
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elif abs_val == 2:
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return (1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
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elif abs_val == 3:
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return (1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
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elif abs_val == 4:
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return (1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
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elif abs_val == 5:
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return (1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
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elif abs_val == 6:
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return (1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0)
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elif abs_val == 7:
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return (1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0)
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elif abs_val == 8:
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return (1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0)
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elif abs_val == 9:
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return (1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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elif abs_val == 10:
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return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0)
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elif abs_val == 11:
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return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0)
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elif abs_val == 12:
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return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0)
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elif abs_val == 13:
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return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0)
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elif abs_val == 14:
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return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0)
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elif abs_val == 15:
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return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
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def bar_trans(board, player):
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if player == 1: return (abs(board[0]/2),)
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elif player == -1: return (abs(board[25]/2),)
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board_rep = []
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for player in [1, -1]:
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for x in board[1:25]:
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board_rep += ordinary_trans(x, player)
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board_rep += bar_trans(board, player)
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board_rep += (15 - Board.num_of_checkers_for_player(board, player),)
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board_rep += ([1, 0] if cur_player == 1 else [0, 1])
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return np.array(board_rep).reshape(1, len(board_rep))
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@staticmethod
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2
main.py
2
main.py
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@ -84,7 +84,7 @@ def log_train_outcome(outcome, diff_in_values, trained_eps = 0, log_path = os.pa
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'sum': sum(outcome),
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'mean': sum(outcome) / len(outcome),
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'time': int(time.time()),
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'average_diff_in_vals': diff_in_values/len(outcome)
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'average_diff_in_vals': diff_in_values
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}
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with open(log_path, 'a+') as f:
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50
network.py
50
network.py
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@ -21,10 +21,10 @@ class Network:
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'quack' : (28, Board.board_features_quack),
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'tesauro' : (198, Board.board_features_tesauro),
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'quack-norm' : (30, Board.board_features_quack_norm),
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'tesauro-fat' : (726, Board.board_features_tesauro_fat),
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'tesauro-poop': (198, Board.board_features_tesauro_wrong)
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}
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def custom_tanh(self, x, name=None):
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return tf.scalar_mul(tf.constant(2.00), tf.tanh(x, name))
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@ -39,6 +39,11 @@ class Network:
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'0': self.make_move_0_ply
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}
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self.max_or_min = {
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1: np.argmax,
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-1: np.argmin
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}
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tf.enable_eager_execution()
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xavier_init = tf.contrib.layers.xavier_initializer()
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@ -144,8 +149,9 @@ class Network:
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:param episode_count:
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:return:
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"""
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tfe.Saver(self.model.variables).save(os.path.join(self.checkpoint_path, 'model.ckpt'))
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#self.saver.save(sess, os.path.join(self.checkpoint_path, 'model.ckpt'), global_step=global_step)
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with open(os.path.join(self.checkpoint_path, "episodes_trained"), 'w+') as f:
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print("[NETWK] ({name}) Saving model to:".format(name=self.name),
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os.path.join(self.checkpoint_path, 'model.ckpt'))
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@ -184,9 +190,6 @@ class Network:
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str(latest_checkpoint))
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tfe.Saver(self.model.variables).restore(latest_checkpoint)
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# variables_names = [v.name for v in self.model.variables]
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# Restore trained episode count for model
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episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
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if os.path.isfile(episode_count_path):
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@ -218,9 +221,9 @@ class Network:
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legal_states = np.array([self.board_trans_func(move, player)[0] for move in legal_moves])
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scores = self.model.predict_on_batch(legal_states)
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transformed_scores = [x if np.sign(player) > 0 else 1 - x for x in scores]
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best_score_idx = np.argmax(np.array(transformed_scores))
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best_score_idx = self.max_or_min[player](scores)
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best_move, best_score = legal_moves[best_score_idx], scores[best_score_idx]
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return (best_move, best_score)
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@ -263,9 +266,10 @@ class Network:
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sorted_moves_and_scores = sorted(moves_and_scores, key=itemgetter(1), reverse=(player == 1))
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best_boards = [ x[0] for x in sorted_moves_and_scores[:10] ]
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scores, trans_scores = self.do_ply(best_boards, player)
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scores = self.do_ply(best_boards, player)
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best_score_idx = np.array(trans_scores).argmax()
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best_score_idx = self.max_or_min[player](scores)
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# best_score_idx = np.array(trans_scores).argmax()
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return (best_boards[best_score_idx], scores[best_score_idx])
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@ -308,7 +312,7 @@ class Network:
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# print(time.time() - start)
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# start = time.time()
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start = time.time()
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all_scores = self.model.predict_on_batch(np.array(test_list))
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@ -319,10 +323,10 @@ class Network:
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from_idx += length
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means_splits = [tf.reduce_mean(scores) for scores in split_scores]
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transformed_means_splits = [x if player == 1 else (1-x) for x in means_splits]
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# print(time.time() - start)
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return (means_splits, transformed_means_splits)
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# print(time.time() - start)
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# print("/"*50)
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return means_splits
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def eval(self, episode_count, trained_eps = 0):
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@ -363,7 +367,6 @@ class Network:
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sys.stderr.write(
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"[EVAL ] Evaluating {eps} episode(s) with method '{method}'\n".format(eps=episodes, method=method))
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if method == 'pubeval':
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outcomes = []
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for i in range(1, episodes + 1):
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@ -454,10 +457,8 @@ class Network:
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:return:
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"""
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difference_in_vals = 0
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self.restore_model()
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average_diffs = 0
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start_time = time.time()
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def print_time_estimate(eps_completed):
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@ -479,26 +480,26 @@ class Network:
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sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
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# TODO decide which player should be here
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player = 1
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# player = 1
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player = random.choice([-1,1])
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prev_board = Board.initial_state
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i = 0
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difference_in_values = 0
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while Board.outcome(prev_board) is None:
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i += 1
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self.global_step += 1
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cur_board, cur_board_value = self.make_move(prev_board,
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(random.randrange(1, 7), random.randrange(1, 7)),
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player)
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difference_in_vals += abs((cur_board_value - self.eval_state(self.board_trans_func(prev_board, player))))
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difference_in_values += abs((cur_board_value - self.eval_state(self.board_trans_func(prev_board, player))))
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if self.config['verbose']:
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print("Difference in values:", difference_in_vals)
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print("Current board value :", cur_board_value)
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print("Current board is :\n",cur_board)
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# adjust weights
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if Board.outcome(cur_board) is None:
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self.do_backprop(self.board_trans_func(prev_board, player), cur_board_value)
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@ -512,6 +513,10 @@ class Network:
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final_score = np.array([Board.outcome(final_board)[1]])
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scaled_final_score = ((final_score + 2) / 4)
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difference_in_values += abs(scaled_final_score-cur_board_value)
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average_diffs += (difference_in_values[0][0] / (i+1))
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self.do_backprop(self.board_trans_func(prev_board, player), scaled_final_score.reshape(1,1))
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sys.stderr.write("\n")
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@ -524,8 +529,9 @@ class Network:
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print_time_estimate(episode)
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sys.stderr.write("[TRAIN] Saving model for final episode...\n")
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self.save_model(episode+trained_eps)
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return outcomes, difference_in_vals[0][0]
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return outcomes, average_diffs/len(outcomes)
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@ -57,4 +57,11 @@ boards = {initial_state,
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# print(network.calculate_1_ply(Board.initial_state, [3,2], 1))
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network.play_against_network()
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diff = [0, 0]
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val = network.eval_state(Board.board_features_quack_fat(initial_state, 1))
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print(val)
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diff[0] += abs(-1-val)
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diff[1] += 1
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print(diff[1])
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