data_analysis_interval: int = 50
save_file_and_plot_update_interval = data_analysis_interval * 1
# data storage settings
save_dir_path: str = SystemConfig.FILE_OUTPUT_HOME_DIR + CommonUtil.generate_date_time_str(
) + "_" + Path(__file__).stem
is_save_raw_data: bool = True
is_save_analytica_data: bool = True
is_save_chart_data: bool = True
# program execution settings
is_print_episode_idx: bool = True
user_input_next_operation: str = "fly"
game_raw_data: GameRawData = GameRawData()
game_raw_data.settings_info_dict["gamma"] = gamma
game_raw_data.settings_info_dict["nn_learning_rate"] = nn_learning_rate
game_raw_data.settings_info_dict[
"data_analysis_interval"] = data_analysis_interval
anay_data: AnalyticalData = AnalyticalData()
anay_data.settings_info_dict = game_raw_data.settings_info_dict
# anay_data.name_datalist_dict["mean"] = [0]
# anay_data.name_datalist_dict["standard_deviation"] = [0]
anay_data.time_step_mean_list = [0]
anay_data.time_step_std_dev_list = [0]
anay_data.reward_mean_list = [0]
anay_data.reward_std_dev_list = [0]
if is_save_chart_data or is_save_analytica_data:
size = 2
random_sample = random.sample([1, 2, 3, 4], size)
print("random_sample", random_sample)
file_path: str = "d:/python_write_file_test.txt"
initial_epsilon = 0.5
decay = 0.5
episodes_per_drop = 500000
discount_rate = 0.9
learning_rate = 0.2
max_episodes = 10000000
game_data: GameRawData = GameRawData()
game_data.add_settings_info("initial_epsilon", initial_epsilon)
game_data.add_settings_info("decay", decay)
game_data.add_settings_info("episodes_per_drop", episodes_per_drop)
game_data.add_settings_info("discount_rate", discount_rate)
game_data.add_settings_info("learning_rate", learning_rate)
game_data.add_settings_info("max_episodes", max_episodes)
# print("game_data.__dict__", game_data.__dict__)
# for i in range(0,1000000):
# game_data.add_end_step_record(i)
# game_data.add_end_reward_record(i)
#
# file1 = open(file_path,"w")
# L = ["This is Delhi \n","This is Paris \n","This is London \n"]
#