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