dqn 100_100_6

env_partion_dist.py

@@ -19,10 +19,10 @@ class PartitionEnv(gym.Env):
         # 可能需要手动修改的超参数
         ##############################
         self.params = 'params_100_100_6'
-        self.ORI_ROW_CUTS = [0, 0.2, 0.5, 0.7, 1]
-        self.ORI_COL_CUTS = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
-        self.CUT_NUM = 12
-        self.BASE_LINE = 19616.68
+        self.ORI_ROW_CUTS = [0, 0.2, 0.4, 0.6, 0.8, 1]
+        self.ORI_COL_CUTS = [0, 0.2, 0.4, 0.5, 0.7, 0.8, 1]
+        self.CUT_NUM = 9
+        self.BASE_LINE = 19757.42
         self.MAX_ADJUST_STEP = 80
         # self.ADJUST_THRESHOLD = 0.1
         # self.mTSP_STEPS = 10000
@@ -94,16 +94,16 @@ class PartitionEnv(gym.Env):
         cut_index, signal = (action + 1) // 2, (action + 1) % 2
         if action == 0:
             pass
-        elif cut_index <= 3:
+        elif cut_index <= 4:
             if signal == 0:
                 self.row_cuts[cut_index] += 0.01
             else:
                 self.row_cuts[cut_index] -= 0.01
         else:
             if signal == 0:
-                self.col_cuts[cut_index-3] += 0.01
+                self.col_cuts[cut_index-4] += 0.01
             else:
-                self.col_cuts[cut_index-3] -= 0.01
+                self.col_cuts[cut_index-4] -= 0.01
 
         # 检查row_cuts和col_cuts是否按升序排列
         if (all(self.row_cuts[i] < self.row_cuts[i+1] for i in range(len(self.row_cuts)-1)) and
@@ -122,8 +122,8 @@ class PartitionEnv(gym.Env):
                 # else:
                 #     # 根据最佳路径计算当前时间
                 #     best_time = self.get_best_time(self.best_path, rectangles)
-                self.best_path = [0, 17, 10, 9, 8, 7, 6, 5, 0, 28, 29, 30, 19, 20, 18, 16, 43, 27, 40, 39, 38, 37,
-                                  36, 26, 45, 14, 13, 12, 11, 22, 21, 23, 24, 41, 44, 25, 34, 35, 33, 32, 31, 42, 15, 4, 3, 2, 1, 0]
+                self.best_path = [33, 30, 29, 28, 27, 21, 15, 0, 13, 7, 1, 2, 31, 14, 8, 3, 4,
+                                  10, 32, 23, 22, 24, 18, 17, 16, 35, 9, 12, 6, 5, 11, 34, 20, 25, 26, 19, 0]
                 best_time = self.get_best_time(self.best_path, rectangles)
 
         else:
@@ -257,9 +257,6 @@ class PartitionEnv(gym.Env):
         # tanh在变量值为2时，就非常接近1了。最大的time_diff为400
         normalized_diff = np.tanh(time_diff / 5000)  # 20是缩放因子，可调整
 
-        # 计算轮次权重（折扣因子）
-        # step_weight = 1 / (1 + np.exp(-self.adjust_step/10))
-
         # 计算最终奖励
         reward = normalized_diff
         # * step_weight  # 10是缩放因子

human_action.py

@@ -13,7 +13,7 @@ print('state:', state)
 # action_series = [1] * 30
 # action_series = [[0.2], [0.4], [0.7], [0.5]]
 # action_series = [[-0.08], [-0.08], [0], [0]]
-action_series = [0, 0, 3, 4, 24, 20]
+action_series = list(range(19))
 
 for i in range(100):
     action = action_series[i]

solutions/dqn_params_100_100_6.json

@@ -0,0 +1,60 @@
+{
+    "best_time": 19643.795059416032,
+    "row_cuts": [
+        0,
+        0.2,
+        0.4,
+        0.6,
+        0.78,
+        1
+    ],
+    "col_cuts": [
+        0,
+        0.2,
+        0.4,
+        0.5,
+        0.7,
+        0.8,
+        1
+    ],
+    "best_path": [
+        33,
+        30,
+        29,
+        28,
+        27,
+        21,
+        15,
+        0,
+        13,
+        7,
+        1,
+        2,
+        31,
+        14,
+        8,
+        3,
+        4,
+        10,
+        32,
+        23,
+        22,
+        24,
+        18,
+        17,
+        16,
+        35,
+        9,
+        12,
+        6,
+        5,
+        11,
+        34,
+        20,
+        25,
+        26,
+        19,
+        0
+    ],
+    "timestamp": "2025-04-05 11:03:20"
+}