基于强化学习的空战辅助决策(2D)_afsim开源代码

作者：凡人多烦事01 | 2024-05-21 07:50:41

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afsim开源代码

1.基本场景

不同与之前的1D问题，这次任务将问题拓展到2D上，如下图只考虑三个作战单元：蓝方战斗机Fighter、红方目标Target、红方导弹防御系统SAM。

在这里插入图片描述

目的：是训练Fighter使其能学到花费最小的时间代价使得Fighter绕过SAM的导弹火力范围，避免火力打击从而直接攻击并击毁Target的启发式原则。
限制：保证Fighter、SAM、Target在场景中的初始位置随机，但基本要使得其相对方位满足上图所示的关系。下表是三者之间的初始位置以及Fighter与SAM之间的火力范围：

在这里插入图片描述

控制：在每个时刻的Fighter的下一个航向(the fighter’s next heading)，是一个连续型控制变量 $[0,2\pi]$ ，是唯一的动作变量。
假设：在此假设Agent只能根据当前状态 $s_t$ 下的每个时间点处的战斗机理想位置采取动作 $a_t$ ，而与之前的Agent的状态 $s_{i}(i=0,1,..t-1)$ 无关。并且在原论文中，为了限制状态空间的尺寸，而将Fighter的速度设置成常量。而在我们这里，将在扩展部分考虑将Fighter的加速度 $a_t$ 也纳入动作空间。同时SAM系统具有部分全向检测的能力，即对于只落在其射程内的Fighter都具有检测能力，而Fighter则对于SAM系统的状态则是全可观测的。
基本平台：原论文中采用的是AFGYM/AFSIM是基本平台。求解算法不再使用Q-Learning，由于其相比与策略梯度方法而言在求解高维状态空间的问题上不具备优势，其在使用带Reinforce的Q-Learning训练行为时，最后的结果不太理想，就像随机游走一样。并且由于决策的实时性导致GAN也是不太适用的，论文采用了A3C和PPO算法对原来问题做了训练。

2.基本模型

2.1 状态空间与动作空间

将原来场景中的状态向量 $s_t$ 由以下的11维向量组成:

在这里插入图片描述

注意：训练过程中除了0-1变量，所有的状态向量的值都被归一化了，否则将限制梯度的反向传播过程。在后面的编程中暂时不考虑SAM pointing、Target heading，因此状态空间被压缩到9维。这个可以作为后面改进的一个具体的方向。其中的Fighter heading作为一个基本的动作 $a_t$ 。

因此最终状态 $s_t$ 的状态空间: $S=[0,1]^{8}$ ,而动作 $a_t$ 的动作空间： $A = [0, 1]$ 映射到角度范围 $180^{。},180^{。}]$ 。

2.2 奖励函数

每当无人机飞行一个时间步长 $\Delta t$ ，若未出现在SAM的火力射程之内，则返回奖励-1,回合继续。若出现在SAM的火力射程之内则返回奖励-50，回合继续。若出现在SAM的火力射程之内且被SAM击落，则返回-1000，回合结束。若出现在Target 附近返回50，回合继续。若出现在Target附近且成功打击到Target，返回1000，回合结束。

2.3 状态转移方程

模型只考虑Fighter匀速运动的情况，后面可以考虑施加突防效果，以此作为一个突破点的研究方向(2)。

假设Fighter的速度恒定为 $v$ ， $t$ 时刻Fighter的航向为 $a_t$ (动作变量)，Fighter的位置为 $x_J^{(t)},y_J^{(t)})$ ，则有：
$x_J^{(t+1)}=x_J^{(t)}+v\Delta t \cos a_t \\ y_J^{(t+1)}=y_J^{(t)}+v\Delta t \sin a_t$
Fighter的弹药量 $w_{t}$ ,如果 $t$ 时刻Fighter打击目标则 $w_{t+1}=w_t-dw_t$ 。在此处我们不考虑弹药量的损失，考虑充分的弹药量，这里也可以作为一个突破点方向(3)。设SAM的火力范围区域为 $R$ ，在这里考虑 $R$ 为以SAM为中心, $R_{SAM}$ 为半径的圆形，其中 $R_{SAM}$ 为Fighter的火力杀伤范围。

3.环境搭建

下面将直接给出代码：

class Fighter:
    def __init__(self,L_limits=100,x=0,y=0,SAM_x=50,SAM_y=50,
                 target_x = 70,target_y=80,velocity=2175.0/3600,
                 delta_t=10,dead=False,SAM_range=40,SAM_time=60,
                 Fighter_range=10,firing_range=3.5,weapon_count=20):
        self.L_limits = L_limits # 限制范围
        self.x = x # 战斗机经度
        self.y = y # 战斗机纬度
        self.SAM_x = SAM_x
        self.SAM_y = SAM_y
        self.target_x = target_x
        self.target_y = target_y
        # 上面是位置的基本属性
        self.SAM_disable = False
        self.SAM_time = SAM_time # SAM系统的预备时间
        self.SAM_range = SAM_range
        self.firing_range = firing_range # 战斗机火力范围
        self.weapon_count = weapon_count # 战斗机弹药容量
        self.velocity = velocity
        self.delta_t = delta_t
        self.dead = dead
        self.wait_time = 0
        self.burn_reward = -1000
        self.win_reward = 1000
        self.reward = -1 # 战斗机正常运行
    # 下面对战斗机的初始位置进行更新
    def reset(self):
        self.x = self.L_limits*np.random.random()
        self.y = self.L_limits*np.random.random()
        self.dead = False
        self.wait_time = 0
        self.SAM_disable = False
        return self.x,self.y
    # 下面是状态转移方程
    def step(self,figher_heading):
        # 下面采用弧度制
        figher_heading = np.clip(figher_heading,-np.pi,np.pi)
        self.x = self.x + self.velocity * self.delta_t*np.cos(figher_heading)
        self.y = self.y + self.velocity * self.delta_t*np.sin(figher_heading)
        # 限制战斗机的横向距离
        if self.x <= 0:
            self.x = 0
        elif self.x > self.L_limits:
            self.x = self.L_limits
        # 限制战斗机的纵向位置
        if self.y <=0:
            self.y = 0
        elif self.y > self.L_limits:
            self.y = self.L_limits
        # 判断战斗机是否在区域内
        if self.SAM_disable == False:
            d = np.sqrt((self.x - self.SAM_x)**2 + (self.y - self.SAM_y)**2)
            if d <= self.SAM_range:
                self.wait_time = self.wait_time + self.delta_t
            else:
                # 一旦不在其SAM射程之内
                self.wait_time = 0
            # 判断战斗机的等待时间
            if self.wait_time >= self.SAM_time: #当战斗机在SAM的时间内停留一段时间
                # 此时战斗机会被击落，返回极大奖励
                self.wait_time = 0
                return (self.x,self.y),self.burn_reward,True
        # 当战斗机靠近SAM时可以立即击毁SAM(这是一个关键节点给50的奖励)
        if d <= self.firing_range and self.SAM_disable == False:
            self.SAM_disable = True
            self.wait_time = 0
            return (self.x,self.y),50,False
        # 当战斗机靠近target时
        if np.sqrt((self.target_x - self.x)**2 + (self.target_y - self.y)**2) <= self.firing_range:
            return (self.x,self.y),self.win_reward,True
        #  战斗机正常运行时候，返回-1
        return (self.x,self.y),-1,False
        ## 强化学习或许可以结合故障树进行策略分析!!!
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下面利用随机游走给出测试代码：

x_array,y_array = [],[]
rewards = 0.0
SAM_x,SAM_y = 50,50
target_x,target_y = 80,70
SAM_range = 20
fighter = Fighter(SAM_x=SAM_x,SAM_y=SAM_y,target_x=target_x,target_y=target_y,SAM_range=SAM_range)
while True:
    fighter_heading = np.random.random()*2*np.pi-np.pi
    next_state,reward,done = fighter.step(fighter_heading)
    rewards += reward
    print("当前Figher位置[{},{}],获得奖励{},累计奖励{}".format(next_state[0],next_state[1],reward,rewards))
    x_array.append(next_state[0])
    y_array.append(next_state[1])
    if done:
        break
import matplotlib.pyplot as plt
# 以火力射程画圆
circle_x,circle_y = [],[]
for theta in np.linspace(-np.pi,np.pi):
    circle_x.append(SAM_x + SAM_range*np.cos(theta))
    circle_y.append(SAM_y + SAM_range*np.sin(theta))
plt.figure(figsize=(5,5))
plt.plot(x_array,y_array,'b.-',linewidth=1)
plt.plot(SAM_x,SAM_y,'ro',linewidth=5)
plt.plot(circle_x,circle_y,'r.-',linewidth=1)
plt.plot(target_x,target_y,'go',linewidth=5)
plt.title(['one road'])
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测试结果为：
以下情况是被SAM击落的：
在这里插入图片描述
以下情况时成功绕开SAM的：

2.实现

采用带优化的连续PPO算法求解上面的问题，需要导入库：

import torch
import matplotlib.pyplot as plt
import numpy as np
from normalization import Normalization, RewardScaling
from replaybuffer import ReplayBuffer
from ppo_continuous import PPO_continuous
from Fighter_env import Fighter
from CPPO_main import args_param,train_network,test_network
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采用下面的主函数求解：

# 声明环境
args = args_param(max_episode_steps=2000,batch_size=64,max_train_steps=5000,K_epochs=3)
# 声明参数
env = Fighter(delta_t=25,SAM_range=30,Fighter_range=10,SAM_time=30)
env.test(show_pictures=True,show_tips=False)
agent = train_network(args,env)
x_array,y_array,_ = test_network(args,env,agent)
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得到以下平均奖励的收敛曲线：
在这里插入图片描述
得到如下的测试结果：

看来用连续动作空间的PPO训练网络还是问题太大了!!!

3.代码

在normalization.py中保存以下代码作为标准化：

import numpy as np

class RunningMeanStd:
    # Dynamically calculate mean and std
    def __init__(self, shape):  # shape:the dimension of input data
        self.n = 0
        self.mean = np.zeros(shape)
        self.S = np.zeros(shape)
        self.std = np.sqrt(self.S)

    def update(self, x):
        x = np.array(x)
        self.n += 1
        if self.n == 1:
            self.mean = x
            self.std = x
        else:
            old_mean = self.mean.copy()
            self.mean = old_mean + (x - old_mean) / self.n
            self.S = self.S + (x - old_mean) * (x - self.mean)
            self.std = np.sqrt(self.S / self.n)

class Normalization:
    def __init__(self, shape):
        self.running_ms = RunningMeanStd(shape=shape)

    def __call__(self, x, update=True):
        # Whether to update the mean and std,during the evaluating,update=False
        if update:
            self.running_ms.update(x)
        x = (x - self.running_ms.mean) / (self.running_ms.std + 1e-8)

        return x

class RewardScaling:
    def __init__(self, shape, gamma):
        self.shape = shape  # reward shape=1
        self.gamma = gamma  # discount factor
        self.running_ms = RunningMeanStd(shape=self.shape)
        self.R = np.zeros(self.shape)

    def __call__(self, x):
        self.R = self.gamma * self.R + x
        self.running_ms.update(self.R)
        x = x / (self.running_ms.std + 1e-8)  # Only divided std
        return x

    def reset(self):  # When an episode is done,we should reset 'self.R'
        self.R = np.zeros(self.shape)

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在Fighter_env.py中写入以下代码,用来作为定战斗机环境：

import numpy as np
import matplotlib.pyplot as plt

# 下面类用来定义环境
class Fighter:
   def __init__(self,L_limits=100,x=0,y=0,SAM_x=50,SAM_y=50,
                target_x = 70,target_y=80,velocity=2175.0/3600,
                delta_t=10,dead=False,SAM_range=40,SAM_time=60,
                Fighter_range=10,weapon_count=20):
       self.L_limits = L_limits # 限制范围
       self.x = x # 战斗机经度
       self.y = y # 战斗机纬度
       self.SAM_x = SAM_x
       self.SAM_y = SAM_y
       self.target_x = target_x
       self.target_y = target_y
       # 上面是位置的基本属性
       self.SAM_disable = False
       self.SAM_time = SAM_time # SAM系统的预备时间
       self.SAM_range = SAM_range
       self.firing_range = Fighter_range # 战斗机火力范围
       self.weapon_count = weapon_count # 战斗机弹药容量
       self.velocity = velocity
       self.delta_t = delta_t
       self.dead = dead
       self.wait_time = 0
       self.burn_reward = -1000
       self.win_reward = 1000
       self.reward = -1 # 战斗机正常运行
       # 下面声明其基本属性
       self.observation_space = np.array([[0,self.L_limits],[0,self.L_limits]])
       self.action_space = np.array([[-np.pi,np.pi]])
   # 下面对战斗机的初始位置进行更新
   def reset(self):
       # self.x = self.L_limits*np.random.random()
       # self.y = self.L_limits*np.random.random()
       self.x = 0
       self.y = 0
       self.dead = False
       self.wait_time = 0
       self.SAM_disable = False
       return np.array([self.x,self.y],dtype=np.float32)
   # 下面是状态转移方程
   def step(self,figher_heading):
       # 下面采用弧度制
       figher_heading = np.clip(figher_heading,-np.pi,np.pi)
       self.x = self.x + self.velocity * self.delta_t*np.cos(figher_heading)
       self.y = self.y + self.velocity * self.delta_t*np.sin(figher_heading)
       self.x = np.clip(self.x,a_min=0.0,a_max=self.L_limits)
       self.y = np.clip(self.y,a_min=0.0,a_max=self.L_limits)
       # 当战斗机靠近target时
       if np.linalg.norm(np.array([self.target_x - self.x, self.target_y - self.y], dtype=np.float32)) <= self.firing_range:
           return np.array([self.x, self.y], dtype=np.float32), self.win_reward, True
       # 判断战斗机是否在区域内
       d = np.linalg.norm(np.array([self.x - self.SAM_x, self.y - self.SAM_y], dtype=np.float32))
       if self.SAM_disable == False:
           if d <= self.SAM_range:
               self.wait_time = self.wait_time + self.delta_t
           else:
               # 一旦不在其SAM射程之内
               self.wait_time = 0
           # 判断战斗机的等待时间
           if self.wait_time >= self.SAM_time: #当战斗机在SAM的时间内停留一段时间
               # 此时战斗机会被击落，返回极大奖励
               self.wait_time = 0
               return  np.array([self.x,self.y],dtype=np.float32),self.burn_reward,True
       # 当战斗机靠近SAM时可以立即击毁SAM(这是一个关键节点给50的奖励)
       if d <= self.firing_range and self.SAM_disable == False:
           self.SAM_disable = True
           self.wait_time = 0
           return  np.array([self.x,self.y],dtype=np.float32),50,False
       #  战斗机正常运行时候，返回-1
       return  np.array([self.x,self.y],dtype=np.float32),-1,False
       ## 强化学习或许可以结合故障树进行策略分析!!!

   # 下面将测试一个回合
   def test(self,show_tips=True,show_pictures=True):
       x_array,y_array=[],[]
       rewards = 0.0
       while True:
           fighter_headings = np.random.random()*2*np.pi - np.pi
           next_state,reward,done = self.step(fighter_headings)
           rewards += reward
           if show_tips:
               print("当前Figher位置[{},{}],获得奖励{},累计奖励{}".format(next_state[0], next_state[1], reward, rewards))
           x_array.append(next_state[0])
           y_array.append(next_state[1])
           if done:
               break
       # 如果要画图就以火力范围画图
       if show_pictures:
           circle_x, circle_y = [], []
           for theta in np.linspace(-np.pi, np.pi):
               circle_x.append(self.SAM_x + self.SAM_range * np.cos(theta))
               circle_y.append(self.SAM_y + self.SAM_range * np.sin(theta))
           plt.figure(figsize=(5, 5))
           plt.plot(x_array, y_array, 'b.-', linewidth=1)
           plt.plot(self.SAM_x, self.SAM_y, 'ro', linewidth=5)
           plt.plot(circle_x, circle_y, 'r.-', linewidth=1)
           plt.plot(self.target_x, self.target_y, 'go', linewidth=5)
           plt.plot(x_array[-1],y_array[-1],'m*',linewidth=5)
           plt.title(['one road'])
           plt.show()
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在replaybuffer.py中存储缓存区replaybuffer类:

    def __init__(self, args):
        self.state_dim = args.state_dim
        self.action_dim = args.action_dim
        self.batch_size = args.batch_size
        self.s = np.zeros((args.batch_size, args.state_dim))
        self.a = np.zeros((args.batch_size, args.action_dim))
        self.a_logprob = np.zeros((args.batch_size, args.action_dim))
        self.r = np.zeros((args.batch_size, 1))
        self.s_ = np.zeros((args.batch_size, args.state_dim))
        self.dw = np.zeros((args.batch_size, 1))
        self.done = np.zeros((args.batch_size, 1))
        self.count = 0

    def store(self, s, a, a_logprob, r, s_, dw, done):
        self.s[self.count] = s
        self.a[self.count] = a
        self.a_logprob[self.count] = a_logprob
        self.r[self.count] = r
        self.s_[self.count] = s_
        self.dw[self.count] = dw
        self.done[self.count] = done
        self.count += 1

    def numpy_to_tensor(self):
        s = torch.tensor(self.s, dtype=torch.float)
        a = torch.tensor(self.a, dtype=torch.float)
        a_logprob = torch.tensor(self.a_logprob, dtype=torch.float)
        r = torch.tensor(self.r, dtype=torch.float)
        s_ = torch.tensor(self.s_, dtype=torch.float)
        dw = torch.tensor(self.dw, dtype=torch.float)
        done = torch.tensor(self.done, dtype=torch.float)
        return s, a, a_logprob, r, s_, dw, done
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在以下文件ppo_continuous.py中包含带优化的连续PPO算法的代码：

import torch
import torch.nn.functional as F
from torch.utils.data.sampler import BatchSampler, SubsetRandomSampler
import torch.nn as nn
from torch.distributions import Beta, Normal

# Trick 8: orthogonal initialization
def orthogonal_init(layer, gain=1.0):
    nn.init.orthogonal_(layer.weight, gain=gain)
    nn.init.constant_(layer.bias, 0)

class Actor_Beta(nn.Module):
    def __init__(self, args):
        super(Actor_Beta, self).__init__()
        self.fc1 = nn.Linear(args.state_dim, args.hidden_width)
        self.fc2 = nn.Linear(args.hidden_width, args.hidden_width)
        self.alpha_layer = nn.Linear(args.hidden_width, args.action_dim)
        self.beta_layer = nn.Linear(args.hidden_width, args.action_dim)
        self.activate_func = [nn.ReLU(), nn.Tanh()][args.use_tanh]  # Trick10: use tanh

        if args.use_orthogonal_init:
            print("------use_orthogonal_init------")
            orthogonal_init(self.fc1)
            orthogonal_init(self.fc2)
            orthogonal_init(self.alpha_layer, gain=0.01)
            orthogonal_init(self.beta_layer, gain=0.01)

    def forward(self, s):
        s = self.activate_func(self.fc1(s))
        s = self.activate_func(self.fc2(s))
        # alpha and beta need to be larger than 1,so we use 'softplus' as the activation function and then plus 1
        alpha = F.softplus(self.alpha_layer(s)) + 1.0
        beta = F.softplus(self.beta_layer(s)) + 1.0
        return alpha, beta

    def get_dist(self, s):
        alpha, beta = self.forward(s)
        dist = Beta(alpha, beta)
        return dist

    def mean(self, s):
        alpha, beta = self.forward(s)
        mean = alpha / (alpha + beta)  # The mean of the beta distribution
        return mean

class Actor_Gaussian(nn.Module):
    def __init__(self, args):
        super(Actor_Gaussian, self).__init__()
        self.max_action = args.max_action
        self.fc1 = nn.Linear(args.state_dim, args.hidden_width)
        self.fc2 = nn.Linear(args.hidden_width, args.hidden_width)
        self.mean_layer = nn.Linear(args.hidden_width, args.action_dim)
        self.log_std = nn.Parameter(torch.zeros(1, args.action_dim))  # We use 'nn.Parameter' to train log_std automatically
        self.activate_func = [nn.ReLU(), nn.Tanh()][args.use_tanh]  # Trick10: use tanh

        if args.use_orthogonal_init:
            print("------use_orthogonal_init------")
            orthogonal_init(self.fc1)
            orthogonal_init(self.fc2)
            orthogonal_init(self.mean_layer, gain=0.01)

    def forward(self, s):
        s = self.activate_func(self.fc1(s))
        s = self.activate_func(self.fc2(s))
        mean = self.max_action * torch.tanh(self.mean_layer(s))  # [-1,1]->[-max_action,max_action]
        return mean

    def get_dist(self, s):
        mean = self.forward(s)
        log_std = self.log_std.expand_as(mean)  # To make 'log_std' have the same dimension as 'mean'
        std = torch.exp(log_std)  # The reason we train the 'log_std' is to ensure std=exp(log_std)>0
        dist = Normal(mean, std)  # Get the Gaussian distribution
        return dist

class Critic(nn.Module):
    def __init__(self, args):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(args.state_dim, args.hidden_width)
        self.fc2 = nn.Linear(args.hidden_width, args.hidden_width)
        self.fc3 = nn.Linear(args.hidden_width, 1)
        self.activate_func = [nn.ReLU(), nn.Tanh()][args.use_tanh]  # Trick10: use tanh

        if args.use_orthogonal_init:
            print("------use_orthogonal_init------")
            orthogonal_init(self.fc1)
            orthogonal_init(self.fc2)
            orthogonal_init(self.fc3)

    def forward(self, s):
        s = self.activate_func(self.fc1(s))
        s = self.activate_func(self.fc2(s))
        v_s = self.fc3(s)
        return v_s

class PPO_continuous():
    def __init__(self, args):
        self.policy_dist = args.policy_dist
        self.max_action = args.max_action
        self.batch_size = args.batch_size
        self.mini_batch_size = args.mini_batch_size
        self.max_train_steps = args.max_train_steps
        self.lr_a = args.lr_a  # Learning rate of actor
        self.lr_c = args.lr_c  # Learning rate of critic
        self.gamma = args.gamma  # Discount factor
        self.lamda = args.lamda  # GAE parameter
        self.epsilon = args.epsilon  # PPO clip parameter
        self.K_epochs = args.K_epochs  # PPO parameter
        self.entropy_coef = args.entropy_coef  # Entropy coefficient
        self.set_adam_eps = args.set_adam_eps
        self.use_grad_clip = args.use_grad_clip
        self.use_lr_decay = args.use_lr_decay
        self.use_adv_norm = args.use_adv_norm

        if self.policy_dist == "Beta":
            self.actor = Actor_Beta(args)
        else:
            self.actor = Actor_Gaussian(args)
        self.critic = Critic(args)

        if self.set_adam_eps:  # Trick 9: set Adam epsilon=1e-5
            self.optimizer_actor = torch.optim.Adam(self.actor.parameters(), lr=self.lr_a, eps=1e-5)
            self.optimizer_critic = torch.optim.Adam(self.critic.parameters(), lr=self.lr_c, eps=1e-5)
        else:
            self.optimizer_actor = torch.optim.Adam(self.actor.parameters(), lr=self.lr_a)
            self.optimizer_critic = torch.optim.Adam(self.critic.parameters(), lr=self.lr_c)

    def evaluate(self, s):  # When evaluating the policy, we only use the mean
        s = torch.unsqueeze(torch.tensor(s, dtype=torch.float), 0)
        if self.policy_dist == "Beta":
            a = self.actor.mean(s).detach().numpy().flatten()
        else:
            a = self.actor(s).detach().numpy().flatten()
        return a

    def choose_action(self, s):
        s = torch.unsqueeze(torch.tensor(s, dtype=torch.float), 0)
        if self.policy_dist == "Beta":
            with torch.no_grad():
                dist = self.actor.get_dist(s)
                a = dist.sample()  # Sample the action according to the probability distribution
                a_logprob = dist.log_prob(a)  # The log probability density of the action
        else:
            with torch.no_grad():
                dist = self.actor.get_dist(s)
                a = dist.sample()  # Sample the action according to the probability distribution
                a = torch.clamp(a, -self.max_action, self.max_action)  # [-max,max]
                a_logprob = dist.log_prob(a)  # The log probability density of the action
        return a.numpy().flatten(), a_logprob.numpy().flatten()

    def update(self, replay_buffer, total_steps):
        s, a, a_logprob, r, s_, dw, done = replay_buffer.numpy_to_tensor()  # Get training data
        """
            Calculate the advantage using GAE
            'dw=True' means dead or win, there is no next state s'
            'done=True' represents the terminal of an episode(dead or win or reaching the max_episode_steps). When calculating the adv, if done=True, gae=0
        """
        adv = []
        gae = 0
        with torch.no_grad():  # adv and v_target have no gradient
            vs = self.critic(s)
            vs_ = self.critic(s_)
            deltas = r + self.gamma * (1.0 - dw) * vs_ - vs
            for delta, d in zip(reversed(deltas.flatten().numpy()), reversed(done.flatten().numpy())):
                gae = delta + self.gamma * self.lamda * gae * (1.0 - d)
                adv.insert(0, gae)
            adv = torch.tensor(adv, dtype=torch.float).view(-1, 1)
            v_target = adv + vs
            if self.use_adv_norm:  # Trick 1:advantage normalization
                adv = ((adv - adv.mean()) / (adv.std() + 1e-5))

        # Optimize policy for K epochs:
        for _ in range(self.K_epochs):
            # Random sampling and no repetition. 'False' indicates that training will continue even if the number of samples in the last time is less than mini_batch_size
            for index in BatchSampler(SubsetRandomSampler(range(self.batch_size)), self.mini_batch_size, False):
                dist_now = self.actor.get_dist(s[index])
                dist_entropy = dist_now.entropy().sum(1, keepdim=True)  # shape(mini_batch_size X 1)
                a_logprob_now = dist_now.log_prob(a[index])
                # a/b=exp(log(a)-log(b))  In multi-dimensional continuous action space，we need to sum up the log_prob
                ratios = torch.exp(a_logprob_now.sum(1, keepdim=True) - a_logprob[index].sum(1, keepdim=True))  # shape(mini_batch_size X 1)

                surr1 = ratios * adv[index]  # Only calculate the gradient of 'a_logprob_now' in ratios
                surr2 = torch.clamp(ratios, 1 - self.epsilon, 1 + self.epsilon) * adv[index]
                actor_loss = -torch.min(surr1, surr2) - self.entropy_coef * dist_entropy  # Trick 5: policy entropy
                # Update actor
                self.optimizer_actor.zero_grad()
                actor_loss.mean().backward()
                if self.use_grad_clip:  # Trick 7: Gradient clip
                    torch.nn.utils.clip_grad_norm_(self.actor.parameters(), 0.5)
                self.optimizer_actor.step()

                v_s = self.critic(s[index])
                critic_loss = F.mse_loss(v_target[index], v_s)
                # Update critic
                self.optimizer_critic.zero_grad()
                critic_loss.backward()
                if self.use_grad_clip:  # Trick 7: Gradient clip
                    torch.nn.utils.clip_grad_norm_(self.critic.parameters(), 0.5)
                self.optimizer_critic.step()

        if self.use_lr_decay:  # Trick 6:learning rate Decay
            self.lr_decay(total_steps)

    def lr_decay(self, total_steps):
        lr_a_now = self.lr_a * (1 - total_steps / self.max_train_steps)
        lr_c_now = self.lr_c * (1 - total_steps / self.max_train_steps)
        for p in self.optimizer_actor.param_groups:
            p['lr'] = lr_a_now
        for p in self.optimizer_critic.param_groups:
            p['lr'] = lr_c_now

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下面代码用来作为训练和测试网络的代码CPPO_main.py的代码：

import matplotlib.pyplot as plt
import numpy as np
from normalization import Normalization, RewardScaling
from replaybuffer import ReplayBuffer
from ppo_continuous import PPO_continuous
from Fighter_env import Fighter

# 先定义一个参数类，用来储存超参数
class args_param(object):
    def __init__(self,max_train_steps=int(3e6),
                evaluate_freq=5e3,
                save_freq=20,
                policy_dist="Gaussian",
                batch_size=2048,
                mini_batch_size=64,
                hidden_width=64,
                lr_a=3e-4,
                lr_c=3e-4,
                gamma=0.99,
                lamda=0.95,
                epsilon=0.2,
                K_epochs=10,
                max_episode_steps = 1000,
                use_adv_norm=True,
                use_state_norm=True,
                use_reward_norm=False,
                use_reward_scaling=True,
                entropy_coef=0.01,
                use_lr_decay=True,
                use_grad_clip=True,
                use_orthogonal_init=True,
                set_adam_eps=True,
                use_tanh=True):
        self.max_train_steps = max_train_steps
        self.evaluate_freq = evaluate_freq
        self.save_freq = save_freq
        self.policy_dist = policy_dist
        self.batch_size = batch_size
        self.mini_batch_size = mini_batch_size
        self.hidden_width = hidden_width
        self.lr_a = lr_a
        self.lr_c = lr_c
        self.gamma = gamma
        self.lamda = lamda
        self.epsilon = epsilon
        self.K_epochs = K_epochs
        self.use_adv_norm = use_adv_norm
        self.use_state_norm = use_state_norm
        self.use_reward_norm = use_reward_norm
        self.use_reward_scaling = use_reward_scaling
        self.entropy_coef = entropy_coef
        self.use_lr_decay = use_lr_decay
        self.use_grad_clip = use_grad_clip
        self.use_orthogonal_init = use_orthogonal_init
        self.set_adam_eps = set_adam_eps
        self.use_tanh = use_tanh
        self.max_episode_steps = max_episode_steps
    def print_information(self):
        print("Maximum number of training steps:",self.max_train_steps)
        print("Evaluate the policy every 'evaluate_freq' steps:",self.evaluate_freq)
        print("Save frequency:",self.save_freq)
        print("Beta or Gaussian:",self.policy_dist)
        print("Batch size:",self.batch_size)
        print("Minibatch size:",self.mini_batch_size)
        print("The number of neurons in hidden layers of the neural network:",self.hidden_width)
        print("Learning rate of actor:",self.lr_a)
        print("Learning rate of critic:",self.lr_c)
        print("Discount factor:",self.gamma)
        print("GAE parameter:",self.lamda)
        print("PPO clip parameter:",self.epsilon)
        print("PPO parameter:",self.K_epochs)
        print("Trick 1:advantage normalization:",self.use_adv_norm)
        print("Trick 2:state normalization:",self.use_state_norm)
        print("Trick 3:reward normalization:",self.use_reward_norm)
        print("Trick 4:reward scaling:",self.use_reward_scaling)
        print("Trick 5: policy entropy:",self.entropy_coef)
        print("Trick 6:learning rate Decay:",self.use_lr_decay)
        print("Trick 7: Gradient clip:",self.use_grad_clip)
        print("Trick 8: orthogonal initialization:",self.use_orthogonal_init)
        print("Trick 9: set Adam epsilon=1e-5:",self.set_adam_eps)
        print("Trick 10: tanh activation function:",self.use_tanh)

# 下面函数用来训练网络
def train_network(args,env,show_picture=True):
    epsiode_rewards = []
    epsiode_mean_rewards = []
    # 下面将导入env环境参数
    args.state_dim = env.observation_space.shape[0]
    args.action_dim = env.action_space.shape[0]
    args.max_action = float(env.action_space[0][1])
    # 下面将定义一个缓冲区
    replay_buffer = ReplayBuffer(args)
    # 下面将定义一个PPO智能体类
    agent = PPO_continuous(args)
    # 下面将采用Trick 2技巧标准化
    state_norm = Normalization(shape=args.state_dim)
    if args.use_reward_norm:
        reward_norm = Normalization(shape=1)
    elif args.use_reward_scaling:  # Trick 4:reward scaling
        reward_scaling = RewardScaling(shape=1, gamma=args.gamma)
    # 下面开始进行训练过程
    for epsiode in range(args.max_train_steps):
        # 每个回合首先对值进行初始化
        epsiode_reward = 0.0
        done = False
        epsiode_count = 0
        # 再赋予一个新的初始状态
        s = env.reset()
        # 对状态进行标准化操作
        if args.use_state_norm:
            s = state_norm(s)
        if args.use_reward_scaling:
            reward_scaling.reset()
        # 设置一个死循环，后面若跳出便在死循环中跳出
        while True:
            # 每执行一个回合，count次数加1
            epsiode_count += 1
            a,a_logprob = agent.choose_action(s)
            # 根据参数的不同选择输出是高斯分布/Beta分布调整
            if args.policy_dist == "Beta":
                action = 2*(a-0.5)*args.max_action
            else:
                action = a
            # 下面是执行环境交互操作
            s_,r,done = env.step(action) ## !!! 这里的环境是自己搭建的，输出每个人都不一样
            s_ = s_.T[0]                 ## !!! 这一部起始没必要，由于我的环境有问题所以加的这一步
            epsiode_reward += r
            # 下面考虑状态标准化的情况
            if args.use_state_norm:
                s_ = state_norm(s_)
            # 下面考虑奖励标准化的可能性
            if args.use_reward_norm:
                r = reward_norm(r)
            elif args.use_reward_scaling:
                r = reward_scaling(r)
            # 下面考虑回合的最大运行次数(只要回合结束或者超过最大回合运行次数)
            if done or epsiode_count >= args.max_episode_steps:
                dw = True
            else:
                dw = False
            # 将经验存入replayBuffer中
            replay_buffer.store(s,action,a_logprob,r,s_,dw,done)
            # 重新赋值状态
            s = s_
            # 当replaybuffer尺寸到达batchsize便会开始训练
            if replay_buffer.count == args.batch_size:
                agent.update(replay_buffer,epsiode)
                replay_buffer.count = 0
            # 如果回合结束便退出
            if done:
                epsiode_rewards.append(epsiode_reward)
                epsiode_mean_rewards.append(np.mean(epsiode_rewards))
                print("第{}次训练的奖励为{:.2f},平均奖励为{:.2f}".format(epsiode,
                                                            epsiode_reward, epsiode_mean_rewards[-1]))
                break
    # 如果需要画图的话
    if show_picture:
        plt.plot(epsiode_rewards)
        plt.plot(epsiode_mean_rewards)
        plt.xlabel("epsiodes")
        plt.ylabel("rewards")
        plt.title("Continuous PPO With Optimization")
        plt.legend(["rewards","mean_rewards"])
        plt.show()
    return agent

# 下面将用训练好的网络跑一次例程
def test_network(args,env,agent,show_pictures=True):
    epsiode_reward = 0.0
    done = False
    epsiode_count = 0
    state_norm = Normalization(shape=args.state_dim)
    # 再赋予一个新的初始状态
    s = env.reset()
    x_array, y_array = [s[0]], [s[1]]
    if args.use_reward_norm:
        reward_norm = Normalization(shape=1)
        s = state_norm(s)
    elif args.use_reward_scaling:  # Trick 4:reward scaling
        reward_scaling = RewardScaling(shape=1, gamma=args.gamma)
        reward_scaling.reset()
    # 设置一个死循环，后面若跳出便在死循环中跳出
    while True:
        # 每执行一个回合，count次数加1
        epsiode_count += 1
        a, a_logprob = agent.choose_action(s)
        # 根据参数的不同选择输出是高斯分布/Beta分布调整
        if args.policy_dist == "Beta":
            action = 2 * (a - 0.5) * args.max_action
        else:
            action = a
        # 下面是执行环境交互操作
        s_, r, done = env.step(action)  ## !!! 这里的环境是自己搭建的，输出每个人都不一样
        epsiode_reward += r
        s_ = s_.T[0]  ## !!! 这一部起始没必要，由于我的环境有问题所以加的这一步
        x_array.append(s_[0])
        y_array.append(s_[1])
        epsiode_reward += r
        # 下面考虑状态标准化的情况
        if args.use_state_norm:
            s_ = state_norm(s_)
        # 重新赋值状态
        s = s_
        # 如果回合结束便退出
        if done or epsiode_count>=2000:
            print("当前测试得分为{}".format(epsiode_reward))
            break
    # 下面开始画图
    if show_pictures:
        circle_x, circle_y = [], []
        for theta in np.linspace(-np.pi, np.pi):
            circle_x.append(env.SAM_x + env.SAM_range * np.cos(theta))
            circle_y.append(env.SAM_y + env.SAM_range * np.sin(theta))
        plt.plot(x_array, y_array, 'b.-', linewidth=1)
        plt.plot(env.SAM_x, env.SAM_y, 'ro', linewidth=5)
        plt.plot(circle_x, circle_y, 'r.-', linewidth=1)
        plt.plot(env.target_x, env.target_y, 'go', linewidth=5)
        plt.plot(x_array[-1], y_array[-1], 'm*', linewidth=5)
        plt.title("one road rewards:{:.2f}".format(epsiode_reward))
        plt.show()
    return x_array,y_array,epsiode_reward
    
# 下面是主函数
if __name__ == "__main__":
    # 声明环境
    args = args_param(max_episode_steps=1000,batch_size=64,max_train_steps=1000,K_epochs=3)
    # 声明参数
    env = Fighter(delta_t=25,SAM_range=30,Fighter_range=10,SAM_time=30)
    env.test(show_pictures=True,show_tips=False)
    agent = train_network(args,env)
    x_array,y_array,_ = test_network(args,env,agent)
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