可视化reward

GA/ga.py

@@ -42,14 +42,14 @@ class GA(object):
         # print("Lens:", len(result), len(result[0]))
         return result
 
-    def greedy_init(self, dis_mat, num_total, num_city):
+    def greedy_init(self):
         start_index = 0
         result = []
-        for i in range(num_total):
+        for i in range(self.num_total):
-            rest = [x for x in range(0, num_city)]
+            rest = [x for x in range(0, self.num_city)]
             # 所有起始点都已经生成了
-            if start_index >= num_city:
+            if start_index >= self.num_city:
-                start_index = np.random.randint(0, num_city)
+                start_index = np.random.randint(0, self.num_city)
                 result.append(result[start_index].copy())
                 continue
             current = start_index
@@ -61,8 +61,8 @@ class GA(object):
                 tmp_choose = -1
                 for x in rest:
                     # print("---", current, x, dis_mat[current][x])
-                    if dis_mat[current][x] < tmp_min:
+                    if self.dis_mat[current][x] < tmp_min:
-                        tmp_min = dis_mat[current][x]
+                        tmp_min = self.dis_mat[current][x]
                         tmp_choose = x
                 if tmp_choose == -1:  # 此种情况仅可能发生在剩的都是基地点
                     tmp_choose = rest[0]

env_partion_dist.py

@@ -19,11 +19,11 @@ 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, 0.4, 0.6, 0.8, 1]
+        self.ORI_ROW_CUTS = [0, 0.28, 0.43, 0.62, 0.77, 1]
         self.ORI_COL_CUTS = [0, 0.2, 0.4, 0.5, 0.7, 0.8, 1]
-        self.CUT_NUM = 9
+        self.CUT_NUM = 5
-        self.BASE_LINE = 19757.42
+        self.BASE_LINE = 19376.06
-        self.MAX_ADJUST_STEP = 80
+        self.MAX_ADJUST_STEP = 50
         # self.ADJUST_THRESHOLD = 0.1
         # self.mTSP_STEPS = 10000
 
@@ -31,7 +31,7 @@ class PartitionEnv(gym.Env):
         self.action_space = spaces.Discrete(self.CUT_NUM*2 + 1)
         # 定义观察空间为8维向量
         self.observation_space = spaces.Box(
-            low=0.0, high=1.0, shape=(self.CUT_NUM + 4,), dtype=np.float32)
+            low=0.0, high=1.0, shape=(len(self.ORI_ROW_CUTS)+len(self.ORI_COL_CUTS),), dtype=np.float32)
 
         self.row_cuts = self.ORI_ROW_CUTS[:]
         self.col_cuts = self.ORI_COL_CUTS[:]
@@ -94,16 +94,16 @@ class PartitionEnv(gym.Env):
         cut_index, signal = (action + 1) // 2, (action + 1) % 2
         if action == 0:
             pass
-        elif cut_index <= 4:
+        elif cut_index <= 5:
             if signal == 0:
-                self.row_cuts[cut_index] += 0.01
+                self.col_cuts[cut_index] += 0.005
             else:
-                self.row_cuts[cut_index] -= 0.01
+                self.col_cuts[cut_index] -= 0.005
         else:
             if signal == 0:
-                self.col_cuts[cut_index-4] += 0.01
+                self.col_cuts[cut_index-4] += 0.005
             else:
-                self.col_cuts[cut_index-4] -= 0.01
+                self.col_cuts[cut_index-4] -= 0.005
 
         # 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

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 = list(range(19))
+action_series = list(range(11))
 
 for i in range(100):
     action = action_series[i]

plot_dqn_training.py

@@ -0,0 +1,40 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+def plot_dqn_training_curve(csv_file):
+    # 读取CSV文件
+    data = pd.read_csv(csv_file)
+
+    # 检查是否包含所需列
+    if 'Step' not in data.columns or 'Value' not in data.columns:
+        raise ValueError("CSV文件必须包含'step'和'value'列")
+
+    # 提取数据
+    steps = data['Step']
+    rewards = data['Value']
+
+    # Plot the curve
+    plt.plot(steps, rewards, label='Reward', color='blue')
+
+    # Set title and axis labels
+    # plt.title("DQN Training Curve", fontsize=16)
+    plt.xlabel("Training Steps (w)", fontsize=14)
+    plt.ylabel("Reward", fontsize=14)
+
+    # Adjust x-axis ticks dynamically based on step range
+    step_min, step_max = steps.min(), steps.max()
+    tick_interval = (step_max - step_min) // 10  # Divide into 10 intervals
+    ticks = np.arange(step_min, step_max + tick_interval, tick_interval)
+    plt.xticks(ticks=ticks, labels=[f"{x//10000}w" for x in ticks])
+
+    # Add grid and legend
+    plt.grid(True, linestyle='--', alpha=0.7)
+
+    # Show the plot
+    plt.show()
+
+if __name__ == "__main__":
+    # 替换为你的CSV文件路径
+    csv_file = r"runs\DQN-PartEnv_S0_ 2025-04-02 20_13\DQN-PartEnv_S0_ 2025-04-02 20_13.csv"
+    plot_dqn_training_curve(csv_file)

solutions/dqn_params_100_100_6.json

@@ -1,11 +1,11 @@
 {
-    "best_time": 19643.795059416032,
+    "best_time": 19376.05694186515,
     "row_cuts": [
         0,
-        0.2,
+        0.2800000000000001,
-        0.4,
+        0.43000000000000005,
-        0.6,
+        0.62,
-        0.78,
+        0.77,
         1
     ],
     "col_cuts": [
@@ -56,5 +56,5 @@
         19,
         0
     ],
-    "timestamp": "2025-04-05 11:03:20"
+    "timestamp": "2025-04-06 09:10:53"
 }

solutions/greedy_params_100_100_5.json

@@ -1,73 +0,0 @@
-{
-    "row_boundaries": [
-        0.0,
-        0.2,
-        0.4,
-        0.6000000000000001,
-        0.8,
-        1.0
-    ],
-    "col_boundaries": [
-        0,
-        0.12533333333333332,
-        0.25066666666666665,
-        0.376,
-        0.5013333333333333,
-        0.6266666666666666,
-        0.7519999999999999,
-        0.8773333333333332,
-        1
-    ],
-    "car_paths": [
-        [
-            0,
-            1,
-            2,
-            3,
-            4,
-            5,
-            6,
-            7
-        ],
-        [
-            8,
-            9,
-            10,
-            11,
-            12,
-            13,
-            14,
-            15
-        ],
-        [
-            16,
-            17,
-            18,
-            19,
-            20,
-            21,
-            22,
-            23
-        ],
-        [
-            24,
-            25,
-            26,
-            27,
-            28,
-            29,
-            30,
-            31
-        ],
-        [
-            32,
-            33,
-            34,
-            35,
-            36,
-            37,
-            38,
-            39
-        ]
-    ]
-}

visualization.py

@@ -14,9 +14,9 @@ def visualize_solution(row_boundaries, col_boundaries, car_paths_coords, W, H, r
     
     # 设置英文标题和标签
     # ax.set_title("Monte Carlo", fontsize=12)
-    ax.set_title("Greedy", fontsize=12)
+    # ax.set_title("Greedy", fontsize=12)
     # ax.set_title("Enumeration-Genetic Algorithm", fontsize=12)
-    # ax.set_title("DQN fine-tuning", fontsize=12)
+    ax.set_title("DQN fine-tuning", fontsize=12)
 
     ax.set_xlabel("Region Width", fontsize=10)
     ax.set_ylabel("Region Height", fontsize=10)
@@ -200,7 +200,7 @@ if __name__ == "__main__":
     # 需要修改的超参数
     # ---------------------------
     params_file = 'params_100_100_6'
-    solution_file = r'solutions\greedy_params_100_100_6.json'
+    solution_file = r'solutions\finetune_params_100_100_6.json'
 
     with open(params_file + '.yml', 'r', encoding='utf-8') as file:
         params = yaml.safe_load(file)