AI技术 - 智能体在智能制造中的优化控制

# 智能制造生产调度智能体示例 import numpy as np import torch import torch.nn as nn from collections import deque import random class ManufacturingSchedulingAgent: def __init__(self, state_dim, action_dim, learning_rate=0.001): self.state_dim = state_dim self.action_dim = action_dim self.memory = deque(maxlen=10000) self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.995 self.learning_rate = learning_rate # 神经网络模型 self.q_network = self.build_q_network() self.target_network = self.build_q_network() self.optimizer = torch.optim.Adam( self.q_network.parameters(), lr=learning_rate ) # 更新目标网络 self.update_target_network() def build_q_network(self): """构建Q网络""" return nn.Sequential( nn.Linear(self.state_dim, 128), nn.ReLU(), nn.Linear(128, 128), nn.ReLU(), nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, self.action_dim) ) def update_target_network(self): """更新目标网络""" self.target_network.load_state_dict( self.q_network.state_dict() ) def remember(self, state, action, reward, next_state, done): """存储经验""" self.memory.append((state, action, reward, next_state, done)) def act(self, state): """选择动作""" if np.random.random() <= self.epsilon: return random.randrange(self.action_dim) state_tensor = torch.FloatTensor(state).unsqueeze(0) q_values = self.q_network(state_tensor) return np.argmax(q_values.cpu().data.numpy()) def replay(self, batch_size=32): """经验回放训练""" if len(self.memory) < batch_size: return batch = random.sample(self.memory, batch_size) states = torch.FloatTensor([e[0] for e in batch]) actions = torch.LongTensor([e[1] for e in batch]) rewards = torch.FloatTensor([e[2] for e in batch]) next_states = torch.FloatTensor([e[3] for e in batch]) dones = torch.BoolTensor([e[4] for e in batch]) current_q_values = self.q_network(states).gather( 1, actions.unsqueeze(1) ) next_q_values = self.target_network(next_states).max(1)[0].detach() target_q_values = rewards + (0.95 * next_q_values * ~dones) loss = nn.MSELoss()(current_q_values.squeeze(), target_q_values) self.optimizer.zero_grad() loss.backward() self.optimizer.step() if self.epsilon > self.epsilon_min: self.epsilon *= self.epsilon_decay # 生产环境示例 class ManufacturingEnvironment: def __init__(self): self.num_machines = 5 self.num_jobs = 10 self.current_time = 0 self.jobs = self.generate_jobs() self.machines = self.initialize_machines() self.completed_jobs = [] self.state_dim = 20 # 状态维度 self.action_dim = 6 # 动作维度 def generate_jobs(self): """生成作业任务""" jobs = [] for i in range(self.num_jobs): job = { 'id': i, 'processing_time': np.random.randint(10, 50), 'due_date': np.random.randint(50, 200), 'priority': np.random.randint(1, 5), 'required_machines': np.random.choice( self.num_machines, size=np.random.randint(1, 4), replace=False ).tolist() } jobs.append(job) return jobs def initialize_machines(self): """初始化机器状态""" machines = [] for i in range(self.num_machines): machine = { 'id': i, 'status': 'idle', # idle, busy, maintenance 'current_job': None, 'completion_time': 0, 'efficiency': np.random.uniform(0.8, 1.0) } machines.append(machine) return machines # 使用示例 if __name__ == "__main__": # 创建环境和智能体 env = ManufacturingEnvironment() agent = ManufacturingSchedulingAgent( env.state_dim, env.action_dim ) print("智能制造调度系统初始化完成！")