Update run_simulation.py

run_simulation.py

@@ -47,6 +47,7 @@
 import math
 import time
 import torch
+import shutil
 
 # 1 init system model
 episode_num = 1
@@ -211,77 +212,81 @@
 
 print("***********************traning information******************************")
 
-
-while episode_cnt < episode_num:
- # 1 reset the whole system
- system.reset()
- step_cnt = 0
- score_per_ep = 0
-
- # 2 get the initial state
- if if_robust:
- tmp = system.observe()
- #z = np.random.multivariate_normal(np.zeros(2), 0.5*np.eye(2), size=len(tmp)).view(np.complex128)
- z = np.random.normal(size=len(tmp))
- observersion_1 = list(
- np.array(tmp) + 0.6 *1e-7* z
- )
- else:
- observersion_1 = system.observe()
- observersion_2 = list(system.UAV.coordinate)
-
- while step_cnt < step_num:
- # 1 count num of step in one episode
- step_cnt += 1
- # judge if pause the whole system
- if not system.render_obj.pause:
- # 2 choose action acoording to current state
- action_1 = agent_1.choose_action(observersion_1, greedy=agent_1_param_dic["action_noise_factor"] * math.pow((1-episode_cnt / episode_num), 2))
- action_2 = agent_2.choose_action(observersion_2, greedy=agent_2_param_dic["action_noise_factor"]* math.pow((1-episode_cnt / episode_num), 2))
- if if_BS:
- action_2[0]=0
- action_2[1]=0
-
- if if_Theta_fixed:
- action_1[0+2 * system.UAV.ant_num * system.user_num:] = len(action_1[0+2 * system.UAV.ant_num * system.user_num:])*[0]
-
- if if_G_fixed:
- action_1[0:0+2 * system.UAV.ant_num * system.user_num]=np.array([-0.0313, -0.9838, 0.3210, 1.0, -0.9786, -0.1448, 0.3518, 0.5813, -1.0, -0.2803, -0.4616, -0.6352, -0.1449, 0.7040, 0.4090, -0.8521]) * math.pow(episode_cnt / episode_num, 2) * 0.7
- #action_1[0:0+2 * system.UAV.ant_num * system.user_num]=len(action_1[0:0+2 * system.UAV.ant_num * system.user_num])*[0.5]
- # 3 get newstate, reward
- if system.if_with_RIS:
- new_state_1, reward, done, info = system.step(
- action_0=action_2[0],
- action_1=action_2[1],
- G=action_1[0:0+2 * system.UAV.ant_num * system.user_num],
- Phi=action_1[0+2 * system.UAV.ant_num * system.user_num:],
- set_pos_x=action_2[0],
- set_pos_y=action_2[1]
- )
- new_state_2 = list(system.UAV.coordinate)
- else:
- new_state_1, reward, done, info = system.step(
- action_0=action_2[0],
- action_1=action_2[1],
- G=action_1[0:0+2 * system.UAV.ant_num * system.user_num],
- set_pos_x=action_2[0],
- set_pos_y=action_2[1]
+try:
+ while episode_cnt < episode_num:
+ # 1 reset the whole system
+ system.reset()
+ step_cnt = 0
+ score_per_ep = 0
+
+ # 2 get the initial state
+ if if_robust:
+ tmp = system.observe()
+ #z = np.random.multivariate_normal(np.zeros(2), 0.5*np.eye(2), size=len(tmp)).view(np.complex128)
+ z = np.random.normal(size=len(tmp))
+ observersion_1 = list(
+ np.array(tmp) + 0.6 *1e-7* z
  )
- new_state_2 = list(system.UAV.coordinate)
-
- score_per_ep += reward
-
- # render
- 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
- time.sleep(0.001) #time.sleep(1)
+ observersion_1 = system.observe()
+ observersion_2 = list(system.UAV.coordinate)
+
+ while step_cnt < step_num:
+ # 1 count num of step in one episode
+ step_cnt += 1
+ # judge if pause the whole system
+ if not system.render_obj.pause:
+ # 2 choose action acoording to current state
+ action_1 = agent_1.choose_action(observersion_1, greedy=agent_1_param_dic["action_noise_factor"] * math.pow((1-episode_cnt / episode_num), 2))
+ action_2 = agent_2.choose_action(observersion_2, greedy=agent_2_param_dic["action_noise_factor"]* math.pow((1-episode_cnt / episode_num), 2))
+ if if_BS:
+ action_2[0]=0
+ action_2[1]=0
+
+ if if_Theta_fixed:
+ action_1[0+2 * system.UAV.ant_num * system.user_num:] = len(action_1[0+2 * system.UAV.ant_num * system.user_num:])*[0]
+
+ if if_G_fixed:
+ action_1[0:0+2 * system.UAV.ant_num * system.user_num]=np.array([-0.0313, -0.9838, 0.3210, 1.0, -0.9786, -0.1448, 0.3518, 0.5813, -1.0, -0.2803, -0.4616, -0.6352, -0.1449, 0.7040, 0.4090, -0.8521]) * math.pow(episode_cnt / episode_num, 2) * 0.7
+ #action_1[0:0+2 * system.UAV.ant_num * system.user_num]=len(action_1[0:0+2 * system.UAV.ant_num * system.user_num])*[0.5]
+ # 3 get newstate, reward
+ if system.if_with_RIS:
+ new_state_1, reward, done, info = system.step(
+ action_0=action_2[0],
+ action_1=action_2[1],
+ G=action_1[0:0+2 * system.UAV.ant_num * system.user_num],
+ Phi=action_1[0+2 * system.UAV.ant_num * system.user_num:],
+ set_pos_x=action_2[0],
+ set_pos_y=action_2[1]
+ )
+ new_state_2 = list(system.UAV.coordinate)
+ else:
+ new_state_1, reward, done, info = system.step(
+ action_0=action_2[0],
+ action_1=action_2[1],
+ G=action_1[0:0+2 * system.UAV.ant_num * system.user_num],
+ set_pos_x=action_2[0],
+ set_pos_y=action_2[1]
+ )
+ new_state_2 = list(system.UAV.coordinate)
+
+ score_per_ep += reward
+
+ # render
+ system.render_obj.render(0.001) # no rendering for faster
+ observersion_1 = new_state_1
+ observersion_2 = new_state_2
+ if done == True:
+ break
 
- system.reset()
- print("ep_num: "+str(episode_cnt)+" ep_score: "+str(score_per_ep))
- episode_cnt +=1
+ else:
+ system.render_obj.render_pause() # no rendering for faster
+ time.sleep(0.001) #time.sleep(1)
+
+ system.reset()
+ print("ep_num: "+str(episode_cnt)+" ep_score: "+str(score_per_ep))
+ episode_cnt +=1
+except KeyboardInterrupt:
+ raise KeyboardInterrupt
+finally:
+ shutil.rmtree('data/storage/data')