这是完整代码import math import random import numpy as np import matplotlib.pyplot as plt #import self as self epsilon = 0.5 gamma = 0.1 lr = 0.1 zeros_vector=[] x = []; y = []; X = []; Y = []; agent=[x,y]; object=[X,Y]; random.seed(70) for i in range(10): x.append(random.uniform(0, 1)) y.append(random.uniform(0, 1)) X.append(random.uniform(1, 10)) Y.append(random.uniform(1, 10)) distance = [] for i in range(len(agent[0])): distance_vector = [] for j in range(len(object[0])): dx = agent[0][i] - object[0][j] dy = agent[1][i] - object[1][j] distance_vector.append(math.sqrt(dx * dx + dy * dy)) distance.append(distance_vector) R_table = np.zeros((10, 10)) for i in range(len(agent[0])): for j in range(len(object[0])): R_table[i,j] = 20-distance[i][j] space = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] #Q_table = [] Q_table = np.zeros((10, 10)) # 进行训练同时测试训练成果 iterate_results = [] # 保存每次测试结果 for i in range(500): print(f"开始第{i + 1}回合。。。") # 初始位置 path = [] # 每个回合要获取10个位置 for j in range(10): remain = set(space) - set(path) # 剩余节点 # s = path[0] # 当前位置 # s_row = Q_table[s] # 当前位置对应的Q表中的行 max_value = -1000 # 在剩余动作中遍历最大值 for rm in remain: Q = Q_table[j][rm] if Q > max_value: max_value = Q a = rm # 随机选择下一行动 if np.random.uniform() < epsilon: a = np.random.choice(np.array(list(set(space) - set(path)))) # 更新Q表 if j != 10: Q_table[j][a] = (1 - lr) * Q_table[j][a] + lr * (R_table[j][a] + gamma * max_value) else: Q_table[j][a] = (1 - lr) * Q_table[j][a] + lr * R_table[j][a] path.append(a) # print(Q_table[j][a]) # 根据当前Q表获取最佳路径距离 result = [] lengths=[] final_length=1000 final_result=[] for k in range(10): loc = k remain1 = set(space) - set(result) # 剩余节点 max_value1 = -1000 # 在剩余节点中遍历最大值 # a = 0 # 利用贪婪策略选择下一行动 for v in remain1: Q_ = Q_table[loc][v] if Q_ > max_value1: a = v max_value1 = Q_ result.append(a) length = 0 for v in range(1, 10): length += distance[result[v - 1]][v-1] print(f"距离为：{length}") if length < final_length: final_length=length final_result=result iterate_results.append(length)

根据您提供的代码，if条件句应该是可以生效的。代码中有一个变量叫做`final_length`，它的初始值为1000。在代码的最后，有一个循环用于计算路径长度，并与`final_length`进行比较，如果路径长度小于`final_length`，则将`final_length`更新为当前路径长度，并将路径保存为`final_result`。所以，如果路径长度小于`final_length`，if条件句应该会被执行。

具体来说，下面这段代码就是if条件句：

if length < final_length:
    final_length=length
    final_result=result

这段代码的作用是，如果当前路径长度`length`小于`final_length`，则将当前路径长度赋值给`final_length`，并将当前路径保存为`final_result`。

如果您有任何其他问题，请随时问我。

相关问题

import numpy as np import matplotlib.pyplot as plt import sympy from scipy.interpolate import interp1d gamma = 1.2 R = 8.314 T0 = 500 Q = 50 * R * T0 a0 = np.sqrt(gamma * R * T0) M0 = 6.216 P_P0 = sympy.symbols('P_P0') num = 81 x0 = np.linspace(0,1,num) t_t0 = np.linspace(0,15,num) x = x0[1:] T_T0 = t_t0[1:] h0 = [] h1 = []#创建拉姆达为1的空数组 r = [] t = [] c = [] s = [] i = 0 for V_V0 in x: n1 = sympy.solve(1 / (gamma-1) * (P_P0 * V_V0 - 1) - 0.5 * (P_P0 + 1) * (1 - V_V0)- gamma * 0 * Q / a0 ** 2,P_P0)#lamuda=0的Hugoniot曲线方程 n2 = sympy.solve(1 / (gamma-1) * (P_P0 * V_V0 - 1) - 0.5 * (P_P0 + 1) * (1 - V_V0)- gamma * 1 * Q / a0 ** 2,P_P0)#lamuda=1的Hugoniot曲线方程 n3 = sympy.solve(-1 * P_P0 + 1 - gamma * M0 ** 2 * (V_V0 - 1),P_P0)#Reyleigh曲线方程 n4 = 12.014556 / V_V0#等温线 n5 = sympy.solve((P_P0 - 1 / (gamma+1) )* (V_V0-gamma / (gamma + 1)) - gamma / ((gamma + 1) ** 2),P_P0)#声速线 n6 = 10.6677 / np.power(V_V0,1.2)#等熵线 h0.append(n1) h1.append(n2) r.append(n3) t.append(n4) c.append(n5) s.append(n6) i = i+1 h0 = np.array(h0) h1 = np.array(h1) r = np.array(r) t = np.array(t) c = np.array(c) s = np.array(s) plt.plot(x,r,label='Rayleigh') plt.plot(x,t,color='purple',label='isothermal') plt.plot(x,s,color='skyblue',label='isentropic') a = np.where(h0 < 0) b = np.where(c < 0) h0 = np.delete(h0,np.where(h0 < 0)[0],axis = 0)#去除解小于0的值 h1 = np.delete(h1,np.where(h1 < 0)[0],axis = 0)#去除解小于0的值 c = np.delete(c,np.where(c < 0)[0],axis = 0)#去除解小于0的值 x0 = np.delete(x,a,axis = 0)#对应去除x轴上错误值的坐标 x1 = np.delete(x,b,axis = 0) plt.plot(x0,h0,label='Hugoniot(lambda=0)') plt.plot(x0,h1,label='Hugoniot(lambda=1)') plt.plot(x1,c,color='yellow',label='soniclocus') plt.ylim((0,50)) plt.legend() # 显示图例 plt.xlabel('V/V0') plt.ylabel('P/P0') f1 = interp1d(x1, c.T, kind='cubic') f2 = interp1d(x,r.T,kind='cubic') f3 = interp1d(x, t.T, kind='cubic') epsilon = 0.0001 x0 = 0.56 y0 = f1(x0) - f2(x0) while abs(y0) > epsilon: df = (f1(x0 + epsilon) - f2(x0 + epsilon) - y0) / epsilon x0 -= y0 / df y0 = f1(x0) - f2(x0) plt.scatter(x0, y0, 50, color ='red') plt.show()

这是一个 Python 代码，我可以解读出来。这段代码主要是用来绘制气体动力学中的一些图形，包括 Hugoniot 曲线、Rayleigh 曲线、等温线、等熵线和声速线等等。其中用到了一些科学计算库，比如 numpy、matplotlib、sympy 和 scipy.interpolate。这段代码还包括了一些数据处理的操作，比如删除数组中小于0的值、插值等等。最后，这段代码还用牛顿迭代法求解了两条曲线的交点。

DQN Pong Pytorch的完整代码

下面是一个使用Pytorch实现DQN算法解决Pong游戏的完整代码：

import gym
import random
import math
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from collections import deque

# 定义神经网络模型
class DQN(nn.Module):
    def __init__(self, input_shape, num_actions):
        super(DQN, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=4, stride=2),
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, stride=1),
            nn.ReLU()
        )
        conv_out_size = self._get_conv_out(input_shape)
        self.fc = nn.Sequential(
            nn.Linear(conv_out_size, 512),
            nn.ReLU(),
            nn.Linear(512, num_actions)
        )

    def _get_conv_out(self, shape):
        o = self.conv(torch.zeros(1, *shape))
        return int(np.prod(o.size()))

    def forward(self, x):
        conv_out = self.conv(x).view(x.size()[0], -1)
        return self.fc(conv_out)

# 定义DQN算法
class DQNAgent:
    def __init__(self, env):
        self.env = env
        self.replay_buffer = deque(maxlen=10000)
        self.gamma = 0.99
        self.epsilon = 1.0
        self.epsilon_decay = 0.995
        self.epsilon_min = 0.01
        self.batch_size = 32
        self.model = DQN(env.observation_space.shape, env.action_space.n).to(device)
        self.target_model = DQN(env.observation_space.shape, env.action_space.n).to(device)
        self.target_model.load_state_dict(self.model.state_dict())
        self.optimizer = optim.Adam(self.model.parameters(), lr=0.00025)

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return self.env.action_space.sample()
        state = torch.FloatTensor(state).unsqueeze(0).to(device)
        q_values = self.model(state)
        return q_values.max(1)[1].item()

    def replay(self):
        if len(self.replay_buffer) < self.batch_size:
            return
        batch = random.sample(self.replay_buffer, self.batch_size)
        states, actions, rewards, next_states, dones = zip(*batch)
        states = torch.FloatTensor(states).to(device)
        actions = torch.LongTensor(actions).to(device)
        rewards = torch.FloatTensor(rewards).to(device)
        next_states = torch.FloatTensor(next_states).to(device)
        dones = torch.FloatTensor(dones).to(device)

        q_values = self.model(states).gather(1, actions.unsqueeze(-1)).squeeze(-1)
        next_q_values = self.target_model(next_states).max(1)[0]
        expected_q_values = rewards + self.gamma * next_q_values * (1 - dones)
        loss = F.mse_loss(q_values, expected_q_values.detach())

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def train(self, num_episodes):
        rewards = []
        for i in range(num_episodes):
            state = env.reset()
            done = False
            episode_reward = 0
            while not done:
                action = self.act(state)
                next_state, reward, done, _ = env.step(action)
                self.replay_buffer.append((state, action, reward, next_state, done))
                state = next_state
                episode_reward += reward
                self.replay()
                self.update_target_model()
            rewards.append(episode_reward)
            self.epsilon = max(self.epsilon_min, self.epsilon * self.epsilon_decay)
            print("Episode {}: reward = {}, epsilon = {}".format(i, episode_reward, self.epsilon))
        return rewards

    def update_target_model(self):
        self.target_model.load_state_dict(self.model.state_dict())

# 设置超参数和设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env = gym.make('Pong-v0')
num_episodes = 1000

# 创建代理并训练
agent = DQNAgent(env)
rewards = agent.train(num_episodes)

# 绘制训练曲线
plt.plot(rewards)
plt.xlabel('Episode')
plt.ylabel('Reward')
plt.title('Training Curve')
plt.show()

注意，在运行代码之前需要安装好Pytorch和Gym库。代码中使用了一个双重Q网络和经验回放的技巧来提高算法的性能和稳定性。在训练结束后，会输出每个回合的奖励和探索率，并绘制训练曲线。