first infer

inference.py

@@ -0,0 +1,122 @@
+import queue
+import threading
+import time
+
+import torch
+
+from train import *
+
+
+def test_one_step(env, act, device, data, action_path):
+    env.rec_data = data
+    state = env.reset()
+    s_tensor = torch.as_tensor((state,), device=device)
+    a_tensor = act(s_tensor)
+    action = a_tensor.detach().cpu().numpy()[0]
+    state, next_state, reward, done = env.step(action)
+    print(f'The action of {env.current_time} is {action}')
+
+    with open(action_path, 'a') as af:
+        af.write(f'{env.current_time},{action}\n')
+    return reward, env.real_unbalance
+
+
+def run_service_test(env, agent, data):
+    service_result_path = 'data/service_result.csv'
+    action_path = 'data/service_actions.csv'
+
+    if not os.path.exists(service_result_path):
+        with open(service_result_path, 'w') as f:
+            f.write('reward,unbalance\n')
+
+    if not os.path.exists(action_path):
+        with open(action_path, 'w') as af:
+            af.write('time,action\n')
+
+    service_rewards = []
+    service_unbalances = []
+
+    service_reward, service_unbalance = test_one_step(env, agent.act, agent.device, data, action_path)
+    service_rewards.append(service_reward)
+    service_unbalances.append(service_unbalance)
+
+    if service_rewards:
+        avg_reward = sum(service_rewards) / len(service_rewards)
+        avg_unbalance = sum(service_unbalances) / len(service_unbalances)
+
+        with open(service_result_path, 'a') as f:
+            f.write(f'{avg_reward},{avg_unbalance}\n')
+
+
+# 接听端
+def listener_thread(env, agent, data_queue):
+    while True:
+        time.sleep(0.1)  # 等待
+        if not data_queue.empty():
+            new_data = data_queue.get()
+            print(f"Data received: {new_data}")
+            run_service_test(env, agent, new_data)
+            data_queue.task_done()
+
+
+# 发送端
+def sender_thread(data_queue):
+    while True:
+        try:
+            user_input = input("请输入price, temper, solar, load, heat, people（用逗号分隔）: ")
+
+            # 将输入字符串分割并转换为浮点数列表
+            input_data = list(map(float, user_input.split(',')))
+
+            # 检查输入是否包含六个数值
+            if len(input_data) != 6:
+                print("输入数据格式不正确，请输入六个数值。")
+                continue
+
+            # 将数据放入队列
+            print(f"Sending data: {input_data}")
+            data_queue.put(input_data)
+
+        except ValueError:
+            print("输入数据格式不正确，请输入数值。")
+
+
+def main():
+    args = Arguments()
+    args.visible_gpu = '0'
+    for seed in args.random_seed_list:
+        args.random_seed = seed
+        args.agent = AgentPPO()
+        args.agent.cri_target = True
+        agent_name = f'{args.agent.__class__.__name__}'
+        args.env = WgzGym()
+        args.init_before_training()
+
+        agent = args.agent
+        env = args.env
+        env.TRAIN = False
+        agent.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate)
+
+    if args.service:
+        args.cwd = agent_name
+        service_act_save_path = f'{args.cwd}/actor.pth'
+        agent.act.load_state_dict(torch.load(service_act_save_path))
+
+        # 创建一个队列用于线程间通信
+        data_queue = queue.Queue()
+
+        listener = threading.Thread(target=listener_thread, args=(env, agent, data_queue))
+        listener.daemon = True
+        listener.start()
+
+        sender = threading.Thread(target=sender_thread, args=(data_queue,))
+        sender.daemon = True
+        sender.start()
+
+        # 主线程保持运行，等待数据传递
+        while True:
+            time.sleep(10)
+
+
+if __name__ == "__main__":
+    main()

models/env.py

@@ -9,6 +9,7 @@ from parameters import *
 class WgzGym(gym.Env):
     def __init__(self, **kwargs):
         super(WgzGym, self).__init__()
+        self.rec_data = None
         self.unbalance = None
         self.reward = None
         self.current_output = None
@@ -58,12 +59,20 @@ class WgzGym(gym.Env):
         grid_ex = self.grid
         time_step = self.current_time
 
-        price = self.data_manager.get_price_data(self.month, self.day, self.current_time)
-        temper = self.data_manager.get_temperature_data(self.month, self.day, self.current_time)
-        solar = self.data_manager.get_solar_data(self.month, self.day, self.current_time)
-        load = self.data_manager.get_load_data(self.month, self.day, self.current_time)
-        heat = self.data_manager.get_heat_data(self.month, self.day, self.current_time)
-        people = self.data_manager.get_people_data(self.month, self.day, self.current_time)
+        if self.TRAIN:
+            price = self.data_manager.get_price_data(self.month, self.day, self.current_time)
+            temper = self.data_manager.get_temperature_data(self.month, self.day, self.current_time)
+            solar = self.data_manager.get_solar_data(self.month, self.day, self.current_time)
+            load = self.data_manager.get_load_data(self.month, self.day, self.current_time)
+            heat = self.data_manager.get_heat_data(self.month, self.day, self.current_time)
+            people = self.data_manager.get_people_data(self.month, self.day, self.current_time)
+        else:
+            price = self.rec_data[0]
+            temper = self.rec_data[1]
+            solar = self.rec_data[2]
+            load = self.rec_data[3]
+            heat = self.rec_data[4]
+            people = self.rec_data[5]
 
         obs = np.concatenate((np.float32(time_step), np.float32(price), np.float32(temper),
                               np.float32(solar), np.float32(load), np.float32(heat),
@@ -78,10 +87,11 @@ class WgzGym(gym.Env):
         self.HST.step(action[1])
         self.grid.step(action[2])
         price = current_obs[1]
-        temper = current_obs[2]
+        temper = current_obs[2]  # 用途待补充
         solar = current_obs[3]
         load = current_obs[4]
         heat = current_obs[5]
+        people = current_obs[6]  # 用途待补充
 
         power_gap = solar + self.HST.get_power() - self.EC.current_power - load
         heat_gap = self.HST.get_heat() + self.EC.get_heat() - heat