text: import os
  import time
  import sys
  import glob
  from gym_idsgame.config.runner_mode import RunnerMode
  from gym_idsgame.agents.training_agents.q_learning.q_agent_config import QAgentConfig
  from gym_idsgame.agents.dao.agent_type import AgentType
  from gym_idsgame.config.client_config import ClientConfig
  from gym_idsgame.runnner import Runner
  from experiments.util import plotting_util, util
  from gym_idsgame.agents.training_agents.q_learning.dqn.dqn_config import DQNConfig
  
  
  def get_script_path():
      """
      :return: the script path
      """
      return os.path.dirname(os.path.realpath(sys.argv[0]))
  
  
  def default_output_dir() -> str:
      """
      :return: the default output dir
      """
      script_dir = get_script_path()
      return script_dir
  
  
  def default_config_path() -> str:
      """
      :return: the default path to configuration file
      """
      config_path = os.path.join(default_output_dir(), './config.json')
      return config_path
  
  
  def default_config() -> ClientConfig:
      """
      :return: Default configuration for the experiment
      """
      dqn_config = DQNConfig(input_dim=44, defender_output_dim=44, hidden_dim=64, replay_memory_size=10000,
                             num_hidden_layers=1,
                             replay_start_size=1000, batch_size=32, target_network_update_freq=1000,
                             gpu=True, tensorboard=True, tensorboard_dir=default_output_dir() + "/results/tensorboard",
                             loss_fn="Huber", optimizer="Adam", lr_exp_decay=True, lr_decay_rate=0.999)
      q_agent_config = QAgentConfig(gamma=0.999, alpha=0.00001, epsilon=1, render=False, eval_sleep=0.9,
                                    min_epsilon=0.01, eval_episodes=100, train_log_frequency=100,
                                    epsilon_decay=0.9999, video=True, eval_log_frequency=1,
                                    video_fps=5, video_dir=default_output_dir() + "/results/videos", num_episodes=20001,
                                    eval_render=False, gifs=True, gif_dir=default_output_dir() + "/results/gifs",
                                    eval_frequency=1000, attacker=False, defender=True, video_frequency=101,
                                    save_dir=default_output_dir() + "/results/data", dqn_config=dqn_config,
                                    checkpoint_freq=5000)
      env_name = "idsgame-random_attack-v9"
      client_config = ClientConfig(env_name=env_name, defender_type=AgentType.DQN_AGENT.value,
                                   mode=RunnerMode.TRAIN_DEFENDER.value,
                                   q_agent_config=q_agent_config, output_dir=default_output_dir(),
                                   title="RandomAttacker vs TrainingDQNAgent",
                                   run_many=True, random_seeds=[0, 999, 299, 399, 499])
      return client_config
  
  
  def write_default_config(path:str = None) -> None:
      """
      Writes the default configuration to a json file
  
      :param path: the path to write the configuration to
      :return: None
      """
      if path is None:
          path = default_config_path()
      config = default_config()
      util.write_config_file(config, path)
  
  
  def plot_csv(config: ClientConfig, eval_csv_path:str, train_csv_path: str, attack_stats_csv_path : str = None,
               random_seed : int = 0) -> None:
      """
      Plot results from csv files
  
      :param config: client config
      :param eval_csv_path: path to the csv file with evaluation results
      :param train_csv_path: path to the csv file with training results
      :param random_seed: the random seed of the experiment
      :param attack_stats_csv_path: path to attack stats
      :return: None
      """
      plotting_util.read_and_plot_results(train_csv_path, eval_csv_path,
                                          config.q_agent_config.train_log_frequency,
                                          config.q_agent_config.eval_frequency, config.q_agent_config.eval_log_frequency,
                                          config.q_agent_config.eval_episodes, config.output_dir, sim=False,
                                          random_seed = random_seed, attack_stats_csv_path = attack_stats_csv_path)
  
  
  def plot_average_results(experiment_title :str, config: ClientConfig, eval_csv_paths:list,
                           train_csv_paths: str) -> None:
      """
      Plots average results after training with different seeds
  
      :param experiment_title: title of the experiment
      :param config: experiment config
      :param eval_csv_paths: paths to csv files with evaluation data
      :param train_csv_paths: path to csv files with training data
      :return: None
      """
      plotting_util.read_and_plot_average_results(experiment_title, train_csv_paths, eval_csv_paths,
                                                  config.q_agent_config.train_log_frequency,
                                                  config.q_agent_config.eval_frequency,
                                                  config.output_dir, plot_attacker_loss = False,
                                                  plot_defender_loss = True)
  
  def run_experiment(configpath: str, random_seed: int, noconfig: bool):
      """
      Runs one experiment and saves results and plots
  
      :param configpath: path to configfile
      :param noconfig: whether to override config
      :return: (train_csv_path, eval_csv_path)
      """
      if configpath is not None and not noconfig:
          if not os.path.exists(args.configpath):
              write_default_config()
          config = util.read_config(args.configpath)
      else:
          config = default_config()
      time_str = str(time.time())
      util.create_artefact_dirs(config.output_dir, random_seed)
      logger = util.setup_logger("dqn_vs_minimal_defense-v9", config.output_dir + "/results/logs/" +
                                 str(random_seed) + "/",
                                 time_str=time_str)
      config.q_agent_config.save_dir = default_output_dir() + "/results/data/" + str(random_seed) + "/"
      config.q_agent_config.video_dir= default_output_dir() + "/results/videos/" + str(random_seed) + "/"
      config.q_agent_config.gif_dir= default_output_dir() + "/results/gifs/" + str(random_seed) + "/"
      config.q_agent_config.dqn_config.tensorboard_dir = default_output_dir() + "/results/tensorboard/" \
                                                         + str(random_seed) + "/"
      config.logger = logger
      config.q_agent_config.logger = logger
      config.q_agent_config.random_seed = random_seed
      config.random_seed = random_seed
      config.q_agent_config.to_csv(config.output_dir + "/results/hyperparameters/" + str(random_seed) + "/" + time_str + ".csv")
      train_result, eval_result = Runner.run(config)
      train_csv_path = ""
      eval_csv_path = ""
      if len(train_result.avg_episode_steps) > 0 and len(eval_result.avg_episode_steps) > 0:
          train_csv_path = config.output_dir + "/results/data/" + str(random_seed) + "/" + time_str + "_train" + ".csv"
          train_result.to_csv(train_csv_path)
          eval_csv_path = config.output_dir + "/results/data/" + str(random_seed) + "/" + time_str + "_eval" + ".csv"
          eval_result.to_csv(eval_csv_path)
          plot_csv(config, eval_csv_path, train_csv_path, random_seed)
  
      return train_csv_path, eval_csv_path
  
  # Program entrypoint
  if __name__ == '__main__':
      args = util.parse_args(default_config_path())
      experiment_title = "random attack vs DQN"
      if args.configpath is not None and not args.noconfig:
          if not os.path.exists(args.configpath):
              write_default_config()
          config = util.read_config(args.configpath)
      else:
          config = default_config()
      if args.plotonly:
          base_dir = default_output_dir() + "/results/data/"
          train_csv_paths = []
          eval_csv_paths = []
          if config.run_many:
              for seed in config.random_seeds:
                  train_csv_path = glob.glob(base_dir + str(seed) + "/*_train.csv")[0]
                  eval_csv_path = glob.glob(base_dir + str(seed) + "/*_eval.csv")[0]
                  attack_stats_csv_path = None
                  try:
                      attack_stats_csv_paths = glob.glob(base_dir + str(seed) + "/attack_stats_*.csv")
                      attack_stats_csv_path = list(filter(lambda x: "checkpoint" not in attack_stats_csv_paths, attack_stats_csv_paths))[0]
                  except:
                      pass
                  train_csv_paths.append(train_csv_path)
                  eval_csv_paths.append(eval_csv_path)
                  plot_csv(config, eval_csv_path, train_csv_path, attack_stats_csv_path, random_seed=seed)
  
              try:
                  plot_average_results(experiment_title, config, eval_csv_paths, train_csv_paths)
              except Exception as e:
                  print("Error when trying to plot summary: " + str(e))
          else:
              train_csv_path = glob.glob(base_dir + str(config.random_seed) + "/*_train.csv")[0]
              eval_csv_path = glob.glob(base_dir + str(config.random_seed) + "/*_eval.csv")[0]
              attack_stats_csv_path = None
              try:
                  attack_stats_csv_paths = glob.glob(base_dir + str(config.random_seed) + "/attack_stats_*.csv")
                  attack_stats_csv_path = \
                  list(filter(lambda x: "checkpoint" not in attack_stats_csv_paths, attack_stats_csv_paths))[0]
              except:
                  pass
              train_csv_paths.append(train_csv_path)
              eval_csv_paths.append(eval_csv_path)
              plot_csv(config, eval_csv_path, train_csv_path, attack_stats_csv_path=attack_stats_csv_path,
                       random_seed=config.random_seed)
      else:
          if not config.run_many:
              run_experiment(args.configpath, 0, args.noconfig)
          else:
              train_csv_paths = []
              eval_csv_paths = []
              for seed in config.random_seeds:
                  train_csv_path, eval_csv_path = run_experiment(args.configpath, seed, args.noconfig)
                  train_csv_paths.append(train_csv_path)
                  eval_csv_paths.append(eval_csv_path)
              try:
                  plot_average_results(experiment_title, config, eval_csv_paths, train_csv_paths)
              except Exception as e:
                  print("Error when trying to plot summary: " + str(e))