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Python强化练习之Tensorflow2 opp算法实现月球登陆器

开发者 https://www.devze.com 2022-12-02 12:04 出处:网络 作者: 我是小白呀
目录概述强化学习算法种类PPO 算法Actor-Critic 算法GymLunarLander-v2启动登陆器PPO 算法实现月球登录器PPOmain输出结果概述
目录
  • 概述
  • 强化学习算法种类
  • PPO 算法
  • Actor-Critic 算法
  • Gym
  • LunarLander-v2
  • 启动登陆器
  • PPO 算法实现月球登录器
    • PPO
    • main
    • 输出结果

概述

从今天开始我们会开启一个新的篇章, 带领大家来一起学习 (卷进) 强化学习 (Reinforcement Learning). 强化学习基于环境, 分析数据采取行动, 从而最大化未来收益.

Python强化练习之Tensorflow2 opp算法实现月球登陆器

强化学习算法种类

Python强化练习之Tensorflow2 opp算法实现月球登陆器

On-policy vs Off-policy:

  • On-policy: 训练数据由当前 agent 不断与环境交互得到
  • Off-policy: 训练的 agent 和与环境交互的 agent 不是同一个 agent, 即别人与环境交互为我提供训练数据

PPO 算法

PPO (Proximal Policy Optimization) 即近端策略优化. PPO 是一种 on-policy 算法, 通过实现小批量更新, 解决了训练过程中新旧策略的变化差异过大导致不易学习的问题.

Python强化练习之Tensorflow2 opp算法实现月球登陆器

Actor-Critic 算法

Actor-Critic 算法共分为两部分. 第一部分为策略函数 Actor, 负责生成动作并与环境交互; 第二部分为价值函数, 负责评估 Actor 的表现.

Python强化练习之Tensorflow2 opp算法实现月球登陆器

Gym

Gym 是一个强化学习会经常用到的包. Gym 里收集了很多游戏的环境. 下面我们就会用 LunarLander-v2 来实现一个自动版的 “阿波罗登月”.

Python强化练习之Tensorflow2 opp算法实现月球登陆器

安装:

pip install gym

如果遇到报错:

AttributeError: module 'gym.envs.box2d' has no attribute 'LunarLander'

解决办法:

pip install gym[box2d]

LunarLander-v2

LunarLander-v2 是一个月球登陆器. 着陆平台位于坐标 (0, 0). 坐标是状态向量的前两个数字, 从屏幕顶部移动到着陆台和零速度的奖励大约是 100 到 140分. 如果着陆器坠毁或停止, 则回合结束, 获得额外的 -100 或 +100点. 每脚接地为 +10, 点火主机每帧 -0.3分, 正解为200分.

Python强化练习之Tensorflow2 opp算法实现月球登陆器

启动登陆器

代码:

import gym

# 创建环境
env = gym.make("LunarLander-v2")

# 重置环境
env.reset()

# 启动
for i in range(180):

    # 渲染环境
    env.render()

    # 随机移动
    observation, reward, done, info = env.step(env.action_space.sample())

    if i % 10 == 0:
        # 调试输出
        print("观察:", observation)
        print("得分:", reward)

输出结果:

观察: [ 0.00861025 1.4061487 0.42930993 -0.11858992 -0.00789343 -0.05729095

0. 0. ]

得分: 0.4097546298543773

观察: [ 0.04917412 1.3876126 0.41002613 -0.13066985 -0.06578191 -0.12604967

0. 0. ]

得分: -1.0858669952763478

观察: [ 0.08917055 1.3429415 0.43598312 -0.2890789 -0.17471936 -0.23913136

0. 0. ]

得分: -2.9339827504803666

观察: [ 0.1326253 1.2450166 0.44708318 -0.5567949 -0.32039645 -0.28250334

0. 0. ]

得分: -2.2779730990326357

观察: [ 0.18323365 1.1110108 0.615291 -0.61922276 -0.43743232 -0.2921057

0. 0. ]

得分: -3.107298313736037

观察: [ 0.24544087 0.94960684 0.66677517 -0.7835077 -0.5929364 -0.2968613

0. 0. ]

得分: -0.5472611013563438

观察: [ 0.3148238 0.75122666 0.7238519 -0.98458177 -0.72915816 -0.26130882

0. 0. ]

得分: -2.5665300894414416

观察: [ 0.38628978 0.49828076 0.74157137 -1.2624744 -0.85754734 -0.37227553

0. 0. ]

得分: -3.2562193227533087

观察: [ 0.46820658 0.18855602 0.92624503 -1.4677961 -1.08614 -0.4508995

0. 0. ]

得分: -4.017106927961208

观察: [ 0.57930076 -0.09440845 1.4345247 -0.693939 -2.0783656 -5.4039164

1. 0. ]

得分: -100

观察: [ 0.7383894 -0.08930686 1.4662493 -0.13461255 -3.653495 -3.109081

0. 0. ]

得分: -100

观察: [ 0.859124 -0.08471288 0.9377837 0.21408719 -3.8998525 0.10151418

0. 0. ]

得分: -100

观察: [ 9.3801367e-01 -4.6761338e-02 6.5999150e-01 1.4583524e-01

-3.9281998e+00 -4.7179851e-06 0.0000000e+00 1.0000000e+00]

得分: -100

观察: [ 0.9879366 -0.04012476 0.33624884 0.08859511 -4.253908 -1.0233303

0. 0. ]

得分: -100

观察: [ 1.0056045 -0.03840658 0.0733737 0.01812508 -4.6796274 -0.6103991

0. 0. ]

得分: -100

观察: [ 1.0112988 -0.03921754 0.07890484 -0.00624387 -4.845023 -0.17111658

0. 0. ]

得分: -100

观察: [ 1.0234139 -0.04488504 0.15701209 -0.0331554 -4.829875 0.07602684

0. 0. ]

得分: -100

观察: [ 1.0306002e+00 -4.8987642e-02 -1.1189224e-02 8.7506004e-04

-4.8712435e+00 -1.5446089e-01 0.0000000e+00 0.0000000e+00]

得分: -100

PPO 算法实现月球登录器

PPO

import numpy as np
import tensorflow as tf
from tensorflow_probability.python.distributions import Categorical


class Memory:
    def __init__(self):
        """初始化"""
        self.actions = []  # 行动(共4种)
        self.states = []  # 状态, 由8个数字组成
        self.logprobs = []  # 概率
        self.rewards = []  # 奖励
        self.is_terminals = []  # 游戏是否结束

    def clear_memory(self):
        """清除memory"""
        del self.actions[:]
        del self.states[:]
        del self.logprobs[:]
        del self.rewards[:]
        del self.is_terminals[:]


class ActorCritic(tf.keras.Model):
    def __init__(self, state_dim, action_dim, n_latent_var):
        super(ActorCritic, self).__init__()

        # 行动
        self.action_layer = tf.keras.Sequential([
            # [b, 8] => [b, 64]
            tf.keras.layers.Dense(n_latent_var, activation="tanh"),

            # [b, 64] => [b, 64]
            tf.keras.layers.Dense(n_latent_var, activation="tanh"),

            # [b, 64] => [b, 4]
            tf.keras.layers.Dense(action_dim, activation="softmax")
        ])

        # 评判
        self.value_layer = tf.keras.Sequential([
            # [b, 8] => [b, 64]
            tf.keras.layers.Dense(n_latent_var, activation="tanh"),

            # [b, 64] => [b, 64]
            tf.keras.layers.Dense(n_latent_var, activation="tanh"),

            # [b, 64] => [b, 1]
            tf.keras.layers.Dense(1)
        ])

    def forward(self):
        """前向传播, 由act替代"""

        raise NotImplementedError

    def build(self, input_shape):

        # No weight to train.
        super(ActorCritic, self).build(input_shape)  # Be sure to call this at the end

    def act(self, state, memory):
        """计算行动"""

        # 计算4个方向概率
        action_probs = self.action_layer(state)

        # 通过最大概率计算最终行动方向
        dist = Categorical(action_probs)
        action = dist.sample()

        # 存入memory
        memory.states.append(state)
        memory.actions.append(action)
        memory.logprobs.append(dist.log_prob(action))

        # 返回行动
        return action.numpy()[0]

    def evaluate(self, state, action):
        """
        评估
        :param state: 状态, 2000个一组, 形状为 [2000, 8]
        :param action: 行动, 2000个一组, 形状为 [2000]
        :return:
        """

        # 计算行动概率
        action_probs = self.action_layer(state)
        dist = Categorical(action_probs)  # 转换成类别分布

        # 计算概率密度, log(概率)
        action_logprobs = dist.log_prob(action)

        # 计算熵
        dist_entropy = dist.entropy()
        dist_entropy = tf.squeeze(dist_entropy)

        # 评判
        state_value = self.value_layer(state)
        state_value = tf.squeeze(state_value)  # [2000, 1] => [2000]


        # 返回行动概率密度, 评判值, 行动概率熵
        return action_logprobs, state_value, dist_entropy


YcizHrVRZtclass PPO:
    def __init__(self, state_dim, action_dim, n_latent_var, lr, betas, gamma, K_epochs, eps_clip):
        self.lr = lr  # 学习率
        self.betas = betas  # betas
        self.gamma = gamma  # gamma
        self.eps_clip = eps_clip  # 裁剪, 限制值范围
        self.K_epochs = K_epochs  # 迭代次数

        # 初始化policy
        self.policy = ActorCritic(state_dim, action_dim, n_latent_var)
        self.policy_old = ActorCritic(state_dim, action_dim, n_latent_var)

        self.optimizer = tf.keras.optimizers.Adam(lr=lr)  # 优化器
        self.MseLoss = tf.keras.losses.MeanSquaredError()  # 损失函数

    def update(self, memory):
        """更新梯度"""

        # 蒙特卡罗预测状态回报
        rewards = []
        discounted_reward = 0
        for reward, is_terminal in zip(reversed(memory.rewards), reversed(memory.is_terminals)):
            # 回合结束
            if is_terminal:
                discounted_reward = 0

            # 更新削减奖励(当前状态奖励 + 0.99*上一状态奖励
            discounted_reward = reward + (self.gamma * discounted_reward)

            # 首插入
            rewards.insert(0, discounted_reward)

        # 标准化奖励
        rewards = tf.convert_to_tensor(rewards, dtype=tf.float32)
        rewards = (rewards - np.mean(rewards)) / (np.std(rewards) + 1e-5)

        # 张量转换
        old_states = tf.stack(memory.states)
        old_actions = tf.stack(memory.actions)
        old_logprobs = tf.stack(memory.logprobs)

        # 迭代优化 K 次:
        for _ in range(self.K_epochs):
            with tf.GradientTape() as tape:

                # 评估
                logprobs, state_values, dist_entropy = self.policy.evaluate(old_states, old_actions)

                # 计算ratios
                ratios = tf.exp(logprobs - old_logprobs)
                ratios = tf.squeeze(ratios)

                # 计算损失
                advantages = rewards - state_values
                surr1 = ratios * advantages
                surr2 = tf.clip_by_value(ratios, 1 - self.eps_clip, 1 + self.eps_clip) * advantages
                loss = -tf.minimum(surr1, surr2) + 0.5 * self.MseLoss(state_values, rewards) - 0.01 * dist_entropy

            # 更新梯度
            grads = tape.gradient(loss, self.policy.action_layer.trainable_variables + self.policy.value_layer.trainable_variables)
            self.optimizer.apply_gradients(zip(grads, self.policy.action_layer.trainable_variables + self.policy.value_layer.trainable_variables))

        # 将新的权重赋值给旧policy
        self.policy_old.action_layer = self.policy.action_layer
        self.policy_old.value_layer = self.policy.value_layer

main

import gym
import tensorflow as tf
from PPO import Memory, PPO

############## 超参数 ##############
env_name = "LunarLander-v2"  # 游戏名字
env = gym.make(env_name)
state_dim = 8  # 状态维度
action_dim = 4  # 行动维度
render = False  # 可视化
solved_reward = 230  # 停止循环条件 (奖励 > 230)
log_interval = 20  # print avg reward in the interval
max_episodes = 50000  # 最大迭代次数
max_timesteps = 300  # 最大单次游戏步数
n_latent_var = 64  # 全连接隐层维度
update_timestep = 2000  # 每2000步policy更新一次
lr = 0.002  # 学习率
betas = (0.9, 0.999)  # betas
gamma = 0.99  # gamma
K_epochs = 4  # policy迭代更新次数
eps_clip = 0.2  # PPO 限幅


#############################################

def main():
    # 实例化
    memory = Memory()
    ppo = PPO(state_dim, action_dim, n_latent_var, lr, betas, gamma, K_epochs, eps_clip)

    # 存放
    total_reward = 0
    total_length = 0
    timestep = 0

    # 训练
    for i_episode in range(1, max_episodes + 1):

        # 环境初始化
        state = env.reset()  # 初始化(重新玩)

        # 转换成tensor
        state = tf.convert_to_tensor(state)
        state = tf.reshape(state, [1, 8])

        # 迭代
        for t in range(max_timesteps):
            timestep += 1

            # 用旧policy得到行动
            action = ppo.policy_old.act(state, memory)

            # 行动
            state, reward, done, _ = env.step(action)  # 得到(新的状态,奖励,是否终止,额外的调试信息)

            # 转换成tensor
            state = tf.convert_to_tensor(state)
            state = tf.reshape(state, [1, 8])

            # 更新memory(奖励/游戏是否结束)
            memory.rewards.append(reward)
            memory.is_terminals.append(done)

            # 更新梯度
            if timestep % update_timestep == 0:
                ppo.update(memory)

                # memory清零
                memory.clear_memory()

                # 累计步数清零
                timestep = 0

            # 累加
            total_reward += reward

            # 可视化
            if render:
                env.render()

            # 如果游戏结束, 退出
            if done:
                break

        # 游戏步长
        total_length += t

        # 如果达到要求(230分), 退出循环
        if total_reward >= (log_interval * solved_reward):
            print("########## Solved! ##########")

            # 保存模型
            tf.keras.models.save_model(ppo.policy.action_layer, r"\model\action")
            tf.keras.models.save_model(ppo.policy.value_layer, r"\model\value")

            # 退出循环
            break

        # 输出log, 每20次迭代
        if i_episode % log_interval == 0:

            # 求20次迭代平均时长/收益
            avg_length = int(total_length / log_interval)
            running_reward = int(total_reward / log_interval)

            # 调试输出
            print('Episode {} \t avg length: {} \t average_reward: {}'.format(i_episode, avg_length, running_reward))

            # 清零
            total_reward = 0
            total_length = 0

if __name__ == '__main__':
    main()

输出结果

Episode 20 avg length: 93 reward: -243

Episode 40 avg length: 92 reward: -172

Episode 60 avg length: 79 reward: -192

Episode 80 avg length: 85 reward: -164

Episode 100 avg length: 90 reward: -179

Episode 120 avg length: 100 reward: -201

Episode 140 avg length: 91 reward: -175

Episode 160 avg length: 101 reward: -141

Episode 180 avg length: 86 reward: -153

Episode 200 avg length: 93 reward: -189

Episode 220 avg length: 96 reward: -221

Episode 240 avg length: 105 reward: -140

Episode 260 avg length: 94 reward: -121

Episode 280 avg length: 91 reward: -131

Episode 300 avg length: 91 reward: -122

Episode 320 avg length: 90 reward: -113

Episode 340 avg length: 100 reward: -110

Episode 360 avg length: 110 reward: -92

Episode 380 avg length: 110 reward: -75

Episode 400 avg length: 119 reward: -76

Episode 420 avg length: 162 reward: -77

Episode 440 avg length: 194 reward: -91

Episode 460 avg length: 144 reward: -28

Episode 480 avg length: 192 reward: -8

Episode 500 avg length: 244 reward: -25

Episode 520 avg length: 239 reward: -1

Episode 540 avg length: 269 reward: 21

Episode 560 avg length: 289 reward: 27

Episode 580 avg length: 270 reward: 65

Episode 600 avg length: 264 reward: 86

Episode 620 avg length: 256 reward: 66

Episode 640 avg length: 278 reward: 75

Episode 660 avg length: 235 reward: 11

Episode 680 avg length: 244 reward: 84

Episode 700 avg length: 253 reward: 73

Episode 720 avg length: 292 reward: 63

Episode 740 avg length: 293 reward: 104

Episode 760 avg length: 279 reward: 109

Episode 780 avg length: 246 reward: 86

Episode 800 avg length: 260 reward: 124

Episode 820 avg length: 276 reward: 131

Episode 840 avg length: 269 reward: 121

Episode 860 avg length: 194 reward: 67

Episode 880 avg length: 241 reward: 94

Episode 900 avg length: 259 reward: 98

Episode 920 avg length: 211 reward: 83

Episode 940 avg length: 260 reward: 105

Episode 960 avg length: 194 reward: 65

Episode 980 avg length: 202 reward: 68

Episode 1000 avg length: 243 reward: 79

Episode 1020 avg length: 260 reward: 66

Episode 1040 avg length: 289 reward: 117

Episode 1060 avg length: 252 reward: 94

Episode 1080 avg length: 262 reward: 114

Episode 1100 avg length: 272 reward: 112

Episode 1120 avg length: 263 reward: 97

Episode 1140 avg length: 256 reward: 93

Episode 1160 avg length: 274 reward: 120

Episode 1180 avg length: 256 reward: 117

Episode 1200 avg length: 241 reward: 105

Episode 1220 avg length: 238 reward: 103

Episode 1240 avg length: 267 reward: 121

Episode 1260 avg length: 283 reward: 124

Episode 1280 avg length: 299 reward: 149

Episode 1300 avg length: 281 reward: 126

Episode 1320 avg length: 266 reward: 102

Episode 1340 avg length: 282 reward: 128

Episode 1360 avg length: 275 reward: 114

Episode 1380 avg length: 285 reward: 105

Episode 1400 avg length: 294 reward: 123

Episode 1420 avg length: 293 reward: 132

Episode 1440 avg length: 248 reward: 85

Episode 1460 avg length: 281 reward: 115

Episode 1480 avg length: 291 reward: 152

Episode 1500 avg length: 279 reward: 130

Episode 1520 avg length: 267 reward: 103

Episode 1540 avg length: 270 reward: 137

Episode 1560 avg length: 269 reward: 120

Episode 1580 avg length: 260 reward: 113

Episode 1600 avg length: 282 reward: 147

Episode 1620 avg length: 259 reward: 125

Episode 1640 avg length: 240 reward: 90

Episode 1660 avg length: 284 reward: 125

Episode 1680 avg length: 282 reward: 123

Episode 1700 avg length: 274 reward: 123

Episode 1720 avg length: 273 reward: 130

Episode 1740 avg length: 260 reward: 117

Episode 1760 avg length: 243 reward: 106

Episode 1780 avg length: 241 reward: 90

Episode 1800 avg length: 290 reward: 144

Episode 1820 avg length: 258 reward: 131

Episode 1840 avg length: 283 reward: 142

Episode 1860 avg length: 262 reward: 100

Episode 1880 avg length: 273 reward: 132

Episode 1900 avg length: 255 reward: 92

Episode 1920 avg length: 251 reward: 117

Episode 1940 avg length: 220 reward: 103

Episode 1960 avg length: 221 reward: 111

Episode 1980 avg length: 205 reward: 83

Episode 2000 avg length: 227 reward: 102

Episode 2020 avg length: 251 reward: 123

Episode 2040 avg length: 227 reward: 100

Episode 2060 avg length: 255 reward: 135

Episode 2080 avg length: 273 reward: 136

Episode 2100 avg length: 256 reward: 126

Episode 2120 avg length: 273 reward: 141

Episode 2140 avg length: 280 reward: 109

Episode 2160 avg length: 266 reward: 112

Episode 2180 avg length: 249 reward: 88

Episode 2200 avg length: 247 reward: 119

Episode 2220 avg length: 270 reward: 143

Episode 2240 avg length: 257 reward: 65

Episode 2260 avg length: 250 reward: 30

Episode 2280 avg length: 261 reward: 112

Episode 2300 avg length: 270 reward: 139

Episode 2320 avg length: 275 reward: 128

Episode 2340 avg length: 290 reward: 149

Episode 2360 avg length: 269 reward: 139

Episode 2380 avg length: 272 reward: 137

Episode 2400 avg length: 232 reward: 105

Episode 2420 avg length: 242 reward: 127

Episode 2440 avg length: 241 reward: 134

Episode 2460 avg length: 249 reward: 113

Episode 2480 avg length: 287 reward: 154

Episode 2500 avg length: 289 reward: 149

Episode 2520 avg length: 258 reward: 129

Episode 2540 avg length: 250 reward: 101

Episode 2560 avg length: 287 reward: 158

Episode 2580 avg length: 271 reward: 145

Episode 2600 avg length: 253 reward: 120

Episode 2620 avg length: 255 reward: 127

Episode 2640 avg length: 254 reward: 122

Episode 2660 avg length: 238 reward: 123

Episode 2680 avg length: 243 reward: 115

Episode 2700 avg length: 241 reward: 93

Episode 2720 avg length: 232 reward: 90

Episode 2740 avg length: 215 reward: 83

Episode 2760 avg length: 241 reward: 112

Episode 2780 avg length: 273 reward: 129

Episode 2800 avg length: 269 reward: 133

Episode 2820 avg length: 246 reward: 91

Episode 2840 avg length: 261 reward: 130

Episode 2860 avg length: 261 reward: 136

Episode 2880 avg length: 289 reward: 128

Episode 2900 avg length: 271 reward: 131

Episode 2920 avg length: 277 reward: 145

Episode 2940 avg length: 251 reward: 117

Episode 2960 avg length: 253 reward: 120

Episode 2980 avg length: 270 reward: 133

Episode 3000 avg length: 240 reward: 85

Episode 3020 avg length: 284 reward: 141

Episode 3040 avg length: 255 reward: 117

Episode 3060 avg length: 299 reward: 134

Episode 3080 avg length: 263 reward: 122

Episode 3100 avg length: 259 reward: 126

Episode 3120 avg length: 270 reward: 125

Episode 3140 avg length: 299 reward: 150

Episode 3160 avg length: 256 reward: 116

Episode 3180 avg length: 264 reward: 124

Episode 3200 avg length: 271 reward: 128

Episode 3220 avg length: 259 reward: 122

Episode 3240 avg length: 261 reward: 125

Episode 3260 avg length: 271 reward: 129

Episode 3280 avg length: 242 reward: 126

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########## Solved! ##########

到此这篇关于Python强化练习之Tensorflow2 opp算法实现月球登陆器的文章就介绍到这了,更多相关Python Tensorflow2 OPP内容请搜索我们以前的文章或继续浏览下面的相关文章希望大家以后多多支持我们!

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