ppo内层加入ga

GA/ga.py

@@ -1,7 +1,7 @@
 import math
 import random
 
-import matplotlib.pyplot as plt
+# import matplotlib.pyplot as plt
 import numpy as np
 
 
@@ -15,7 +15,7 @@ class GA(object):
         self.location = data
         self.to_process_idx = to_process_idx
         self.rectangles = rectangles
-        self.epochs = 3000
+        self.epochs = 500
         self.ga_choose_ratio = 0.2
         self.mutate_ratio = 0.05
         # fruits中存每一个个体是下标的list

GA/main_parallel.py

@@ -30,10 +30,10 @@ if __name__ == "__main__":  # 重要：在 Windows 上必须加这一行
     # ---------------------------
     # 需要修改的超参数
     # ---------------------------
-    R = 3
+    R = 1
-    C = 3
+    C = 1
-    params_file = 'params2'
+    params_file = 'params3'
-    batch_size = 60  # 控制一次最多并行多少个任务
+    batch_size = 10  # 控制一次最多并行多少个任务
 
     with open(params_file + '.yml', 'r', encoding='utf-8') as file:
         params = yaml.safe_load(file)

env_partion.py

@@ -4,6 +4,7 @@ import numpy as np
 import yaml
 import math
 from mTSP_solver import mTSP
+from GA.ga import GA
 
 
 class PartitionEnv(gym.Env):
@@ -17,11 +18,11 @@ class PartitionEnv(gym.Env):
         ##############################
         # 可能需要手动修改的超参数
         ##############################
-        self.params = 'params2'
+        self.params = 'params3'
-        self.CUT_NUM = 4
+        self.CUT_NUM = 2
-        self.ROW_CUT_LIMIT = 3
+        self.ROW_CUT_LIMIT = 1
         self.COL_CUT_LIMIT = 1
-        self.BASE_LINE = 12000
+        self.BASE_LINE = 5000
         self.mTSP_STEPS = 10000
 
         # 车队参数设置
@@ -138,22 +139,41 @@ class PartitionEnv(gym.Env):
                 state = self.partition_values
 
                 # 继续进行路径规划
+                # 使用q_learning解多旅行商
                 # cities: [[x1, x2, x3...], [y1, y2, y3...]] 城市坐标
-                rec_center_lt = [rec_info['center']
+                # rec_center_lt = [rec_info['center']
-                                 for rec_info in self.rectangles]
+                #                  for rec_info in self.rectangles]
-                cities = np.column_stack(rec_center_lt)
+                # cities = np.column_stack(rec_center_lt)
-                cities = np.column_stack((self.center, cities))
+                # cities = np.column_stack((self.center, cities))
 
-                center_idx = []
+                # center_idx = []
+                # for i in range(self.num_cars - 1):
+                #     cities = np.column_stack((cities, self.center))
+                #     center_idx.append(cities.shape[1] - 1)
+
+                # tsp = mTSP(params=self.params, num_cities=cities.shape[1], cities=cities, num_cars=self.num_cars,
+                #            center_idx=center_idx, rectangles=self.rectangles)
+
+                # best_time, best_path = tsp.train(self.mTSP_STEPS)
+
+                # 使用遗传算法解多旅行商
+                cities = [self.center]
+                for rec in self.rectangles:
+                    cities.append(rec['center'])
+                cities = np.array(cities)
+
+                center_idx = [0]
                 for i in range(self.num_cars - 1):
-                    cities = np.column_stack((cities, self.center))
+                    cities = np.row_stack((cities, self.center))
-                    center_idx.append(cities.shape[1] - 1)
+                    center_idx.append(cities.shape[0] - 1)
 
-                tsp = mTSP(params=self.params, num_cities=cities.shape[1], cities=cities, num_cars=self.num_cars,
+                ga = GA(num_drones=self.num_cars, num_city=cities.shape[0], num_total=20,
-                           center_idx=center_idx, rectangles=self.rectangles)
+                        data=cities, to_process_idx=center_idx, rectangles=self.rectangles)
-                best_time, best_path = tsp.train(self.mTSP_STEPS)
+
-                print(best_time)
+                best_path, best_time = ga.run()
-                print(best_path)
+
+                # print(best_time)
+                # print(best_path)
 
                 reward += self.BASE_LINE - best_time
 

human_action.py

@@ -11,6 +11,7 @@ print('state:', state)
 # action_series = [[0.67], [0], [0], [0], [0.7]]
 # action_series = [0, 0, 3, 0, 10]
 action_series = [[0.2], [0.4], [0.7], [0.5]]
+# action_series = [[0.5], [0.5]]
 
 for i in range(100):
     action = action_series[i]

mTSP_solver.py

@@ -15,9 +15,9 @@ class mTSP(object):
                  num_cars=2,
                  center_idx=[0],
                  rectangles=None,
-                 alpha=2,
+                 alpha=1,
-                 beta=1,
+                 beta=4,
-                 learning_rate=0.001,
+                 learning_rate=0.01,
                  eps=0.1,
                  ):
         '''

solutions/trav_ga_params2_parallel.json

@@ -1,7 +1,7 @@
 {
     "row_boundaries": [
         0.0,
-        0.2,
+        0.1,
         0.4,
         0.7,
         1.0
@@ -18,13 +18,13 @@
             5
         ],
         [
-            4,
+            0,
             2,
-            0
+            4
         ],
         [
-            7,
+            6,
-            6
+            7
         ]
     ]
 }

solutions/trav_ga_params3_parallel.json

@@ -11,8 +11,8 @@
     ],
     "car_paths": [
         [
-            2,
+            0,
-            0
+            2
         ],
         [
             3,