Update run_simulation.py

run_simulation.py

@@ -231,8 +231,7 @@
  else:
  observersion_1 = system.observe()
  observersion_2 = list(system.UAV.coordinate)
- if episode_cnt == 80:
- print("break point")
+
  while step_cnt < step_num:
  # 1 count num of step in one episode
  step_cnt += 1
@@ -273,16 +272,8 @@
  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()
- '''
-
+
+ # render
  system.render_obj.render(0.001) # no rendering for faster
  observersion_1 = new_state_1
  observersion_2 = new_state_2
@@ -292,16 +283,7 @@
  else:
  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
- if episode_cnt % 10 == 0:
- agent_1.save_models()
- agent_2.save_models()
-
-# save the last model
-agent_1.save_models()
-agent_2.save_models()