text: import argparse
  import os
  import sys
  from datetime import datetime
  
  import attr
  
  if 'SUMO_HOME' in os.environ:
      tools = os.path.join(os.environ['SUMO_HOME'], 'tools')
      sys.path.append(tools)
  else:
      sys.exit("Please declare the environment variable 'SUMO_HOME'")
  
  from sumo_rl import SumoEnvironment
  from sumo_rl.agents import QLAgent
  from sumo_rl.exploration import EpsilonGreedy
  
  
  @attr.s
  class QLearningParams:
      route_file = attr.ib(default='data/osm-train-simple/osm.rou.xml')
      net_file = attr.ib(default='data/osm-train-simple/map.net.xml')
      out_file = attr.ib(default='outputs/osm-train-simple/')
      simulation_tile = attr.ib(default=100000)
      min_green_time = attr.ib(default=10)
      max_green_time = attr.ib(default=45)
      alpha_learning_rate = attr.ib(default=0.1)
      gamma_discount_rate = attr.ib(default=0.99)
      epsilon = 0.05
  
      def format_out_path(self, time, alpha, gamma, epsilon, decay, reward) -> str:
          return (self.out_file+'{}_alpha-{}_gamma-{}_eps-{}_decay-{}_reward-{}').format(time, alpha, gamma, epsilon, decay, reward)
  
      def get_q_learning_args(self):
          prs = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter,
                                        description="""Q-Learning Single-Intersection""")
          prs.add_argument("-route", dest="route", type=str, default=self.route_file,
                           help="Route definition xml file.\n")
          prs.add_argument("-a", dest="alpha", type=float, default=self.alpha_learning_rate, required=False,
                           help="Alpha learning rate.\n")
          prs.add_argument("-g", dest="gamma", type=float, default=self.gamma_discount_rate, required=False,
                           help="Gamma discount rate.\n")
          prs.add_argument("-e", dest="epsilon", type=float, default=self.epsilon, required=False, help="Epsilon.\n")
          prs.add_argument("-me", dest="min_epsilon", type=float, default=0.005, required=False,
                           help="Minimum epsilon.\n")
          prs.add_argument("-d", dest="decay", type=float, default=1.0, required=False, help="Epsilon decay.\n")
          prs.add_argument("-mingreen", dest="min_green", type=int, default=self.min_green_time, required=False,
                           help="Minimum green time.\n")
          prs.add_argument("-maxgreen", dest="max_green", type=int, default=self.max_green_time, required=False,
                           help="Maximum green time.\n")
          prs.add_argument("-gui", action="store_true", default=True, help="Run with visualization on SUMO.\n")
          prs.add_argument("-fixed", action="store_true", default=False, help="Run with fixed timing traffic signals.\n")
          prs.add_argument("-s", dest="seconds", type=int, default=self.simulation_tile, required=False,
                           help="Number of simulation seconds.\n")
          prs.add_argument("-r", dest="reward", type=str, default='wait', required=False,
                           help="Reward function: [-r queue] for average queue reward or [-r wait] for waiting time reward.\n")
          prs.add_argument("-runs", dest="runs", type=int, default=1, help="Number of runs.\n")
          return prs.parse_args()
  
  
  if __name__ == '__main__':
      params = QLearningParams()
      args = params.get_q_learning_args()
      experiment_time = str(datetime.now()).split('.')[0]
      out_csv = params.format_out_path(experiment_time, args.alpha, args.gamma, args.epsilon, args.decay, args.reward)
  
      env = SumoEnvironment(net_file=params.net_file,
                            route_file=args.route,
                            out_csv_name=out_csv,
                            use_gui=args.gui,
                            num_seconds=args.seconds,
                            min_green=args.min_green,
                            max_green=args.max_green,
                            sumo_warnings=False)
  
      for run in range(1, args.runs + 1):
          initial_states = env.reset()
          ql_agents = {ts: QLAgent(starting_state=env.encode(initial_states[ts], ts),
                                   state_space=env.observation_space,
                                   action_space=env.action_space,
                                   alpha=args.alpha,
                                   gamma=args.gamma,
                                   exploration_strategy=EpsilonGreedy(initial_epsilon=args.epsilon,
                                                                      min_epsilon=args.min_epsilon, decay=args.decay)) for
                       ts in env.ts_ids}
  
          done = {'__all__': False}
          infos = []
          if args.fixed:
              while not done['__all__']:
                  _, _, done, _ = env.step({})
          else:
              while not done['__all__']:
                  actions = {ts: ql_agents[ts].act() for ts in ql_agents.keys()}
  
                  s, r, done, _ = env.step(action=actions)
  
                  for agent_id in ql_agents.keys():
                      ql_agents[agent_id].learn(next_state=env.encode(s[agent_id], agent_id), reward=r[agent_id])
          env.save_csv(out_csv, run)
          env.close()
  