Python强化练习之Tensorflow2 opp算法实现月球登陆器

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.

强化学习算法种类

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.

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.

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”.

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.

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

Episode 3300 avg length: 218 reward: 93

Episode 3320 avg length: 230 reward: 116

Episode 3340 avg length: 223 reward: 109

Episode 3360 avg length: 249 reward: 122

Episode 3380 avg length: 224 reward: 104

Episode 3400 avg length: 261 reward: 131

Episode 3420 avg length: 280 reward: 140

Episode 3440 avg length: 264 reward: 125

Episode 3460 avg length: 247 reward: 105

Episode 3480 avg length: 276 reward: 141

Episode 3500 avg length: 282 reward: 149

Episode 3520 avg length: 282 reward: 141

Episode 3540 avg length: 290 reward: 152

Episode 3560 avg length: 282 reward: 141

Episode 3580 avg length: 291 reward: 151

Episode 3600 avg length: 289 reward: 166

Episode 3620 avg length: 266 reward: 142

Episode 3640 avg length: 277 reward: 91

Episode 3660 avg length: 272 reward: 114

Episode 3680 avg length: 281 reward: 159

Episode 3700 avg length: 287 reward: 160

Episode 3720 avg length: 254 reward: 78

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Episode http://www.cppcns.com15620 avg length: 254 reward: 88

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Episode 16500 avg length: 273 reward: 148

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Episode 16900 avg length: 268 reward: 133

Episode 16920 avg length: 291 reward: 147

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Episode 16960 avg length: 281 reward: 137

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Episode 17000 avg length: 261 reward: 135

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Episode 17040 avg length: 274 reward: 176

Episode 17060 avg length: 262 reward: 131

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Episode 17360 avg length: 192 reward: 164

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

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