text: import numpy as np
  from gym_collision_avoidance.envs.policies.ExternalPolicy import ExternalPolicy
  
  class CARRLPolicy(ExternalPolicy):
      def __init__(self):
          ExternalPolicy.__init__(self, str="CARRL")
          num_actions = 11
          max_heading_change = np.pi / 6
          self.actions = np.zeros((num_actions, 2)) # two for vel, heading 
          self.actions[:, 0] = np.ones((num_actions,))  # vel
          self.actions[:, 1] = np.linspace(
              -max_heading_change, max_heading_change, num_actions)  # vel
  
      def convert_to_action(self, discrete_action):
          return self.actions[discrete_action,:]