def EMD_between_two_models_on_board(model1_name,
input_plains_num_1,
i1,
model2_name,
input_plains_num_2,
i2,
board1,
board2,
width=6,
height=6,
use_gpu=True):
model_file_1 = f'/home/lirontyomkin/AlphaZero_Gomoku/models/{model1_name}/current_policy_{i1}.model'
policy_1 = PolicyValueNet(width,
height,
model_file=model_file_1,
input_plains_num=input_plains_num_1,
use_gpu=use_gpu)
model_file_2 = f'/home/lirontyomkin/AlphaZero_Gomoku/models/{model2_name}/current_policy_{i2}.model'
policy_2 = PolicyValueNet(width,
height,
model_file=model_file_2,
input_plains_num=input_plains_num_2,
use_gpu=use_gpu)
board_current_state1 = board1.current_state(last_move=True,
is_random_last_turn=False)
board_current_state2 = board2.current_state(last_move=True,
is_random_last_turn=False)
acts_policy1, probas_policy1 = zip(*policy_1.policy_value_fn(board1)[0])
acts_policy2, probas_policy2 = zip(*policy_2.policy_value_fn(board2)[0])
dist_matrix = generate_matrix_dist_metric(width)
distance = emd(np.asarray(probas_policy1, dtype='float64'),
np.asarray(probas_policy2, dtype='float64'), dist_matrix)
return distance
def threshold_cutoff_policy(board,
model_name,
input_plains_num,
model_iteration,
open_path_threshold,
opponent_weight,
rounding=-1,
cutoff_threshold=0.05,
model_file=None,
is_random_last_turn=False,
board_name=" "):
# print(f"{model_name}_{model_iteration}, {open_path_threshold}, {opponent_weight}, {cutoff_threshold}")
if is_random_last_turn:
model_namee = model_name + "_random"
else:
model_namee = model_name
width, height = board.width, board.height
if model_file is None:
model_file = f'/home/lirontyomkin/AlphaZero_Gomoku/models/{model_name}/current_policy_{model_iteration}.model'
policy = PolicyValueNet(width,
height,
model_file=model_file,
input_plains_num=input_plains_num)
board.set_is_random_last_turn(is_random_last_turn=is_random_last_turn,
player=board.get_current_player())
if is_random_last_turn:
board.set_random_seed(model_iteration)
acts_policy, probas_policy = zip(*policy.policy_value_fn(board)[0])
# AlphaZero gives some probability to locations that are not available for some reason
if np.sum(probas_policy) != 0:
probas_policy = probas_policy / np.sum(probas_policy)
move_probs_policy = np.zeros(width * height)
move_probs_policy[list(acts_policy)] = probas_policy
move_probs_policy = move_probs_policy.reshape(width, height)
move_probs_policy = np.flipud(move_probs_policy)
if cutoff_threshold < 1:
move_probs_policy[move_probs_policy < cutoff_threshold] = 0
heatmap_save_path = f"/home/lirontyomkin/AlphaZero_Gomoku/models_heatmaps/cutoff_threshold_{cutoff_threshold}/{model_namee}/iteration_{model_iteration}/"
elif isinstance(cutoff_threshold, int):
move_probs_policy = keep_k_squares(move_probs_policy, cutoff_threshold,
board.height, board.width)
heatmap_save_path = f"/home/lirontyomkin/AlphaZero_Gomoku/models_heatmaps/keep_{cutoff_threshold}_squares/{model_namee}/iteration_{model_iteration}/"
move_probs_policy = normalize_matrix(move_probs_policy, board, rounding)
# if not os.path.exists(heatmap_save_path):
# os.makedirs(heatmap_save_path)
# #make sure you save once:
# if open_path_threshold == -1 and opponent_weight == 0:
# save_trimmed_policy_heatmap(move_probs_policy, model_name, board, board_name, heatmap_save_path)
return move_probs_policy