Update run_simulation.py

run_simulation.py

@@ -273,24 +273,28 @@
  new_state_2 = list(system.UAV.coordinate)
 
  score_per_ep += reward
+ '''
  # 4 store state pair into mem pool
  agent_1.remember(observersion_1, action_1, reward, new_state_1, int(done))
  agent_2.remember(observersion_2, action_2, reward, new_state_2, int(done))
  # 5 update DDPG net
  agent_1.learn()
  if not TRAINED_UAV:
  agent_2.learn()
+ '''
 
- #system.render_obj.render(0.001) # no rendering for faster
+ system.render_obj.render(0.001) # no rendering for faster
  observersion_1 = new_state_1
  observersion_2 = new_state_2
  if done == True:
  break
 
  else:
- #system.render_obj.render_pause() # no rendering for faster
+ system.render_obj.render_pause() # no rendering for faster
  time.sleep(0.001) #time.sleep(1)
+ '''
  system.data_manager.save_file(episode_cnt=episode_cnt)
+ '''
  system.reset()
  print("ep_num: "+str(episode_cnt)+" ep_score: "+str(score_per_ep))
  episode_cnt +=1