text: import numpy as np
  
  
  def get_settings(name, mode):
  
      safe_dist_lower = 2
      safe_dist_upper = 10
      safe_power = 100
      robustness_dist = f'G(dist <= {safe_dist_upper} & dist >= {safe_dist_lower})'
      robustness_power = f'G(power <= {safe_power})'
      alpha=0.98 # distance robustness weight
  
      # PARAMS GRID
      
      if mode=="train":
          agent_position = np.random.uniform(0, 2, 100)
          leader_position = np.random.uniform(4, 6, 100)
          agent_velocity = np.random.uniform(15, 20, 100)
          leader_velocity = np.random.uniform(15, 20, 100)
  
      elif mode=="test":
          agent_position = np.random.uniform(0, 2, 150)
          leader_position = np.random.uniform(4, 6, 150)
          agent_velocity = np.random.uniform(15, 17, 150)
          leader_velocity = np.random.uniform(17, 20, 150)
  
      # ARCHITECTURE
  
      if name=="default":
  
          atk_arch = {'hidden':1, 'size':10, 'coef':1, 'noise':2}
          def_arch = {'hidden':2, 'size':10, 'coef':1}
          train_par = {'train_steps':1000, 'atk_steps':1, 'def_steps':2, 'horizon':2., 'dt': 0.05, 'lr':.001}
          test_par = {'test_steps':200, 'dt':0.05}
          
      else:
          raise NotImplementedError
  
      return agent_position, agent_velocity, leader_position, leader_velocity, \
              atk_arch, def_arch, train_par, test_par, robustness_dist, robustness_power, \
              safe_dist_lower, safe_dist_upper, safe_power, alpha
  