text: from agents.rl_drone import RLAgent
  from agents.drone_sim_env import drone_sim
  
  env = drone_sim()
  agent = RLAgent(env)
  ENV_NAME = 'drone'
  agent.agent.fit(env, nb_steps=100000, visualize=True, verbose=1, nb_max_episode_steps=10)
  
  #After training is done, we save the final weights.
  agent.agent.save_weights('ddpg_{}_weights.h5f'.format(ENV_NAME), overwrite=True)
  #agent.agent.load_weights('ddpg_{}_weights.h5f'.format(ENV_NAME))
  
  # Finally, evaluate our algorithm for 5 episodes.