Python强化练习之PyTorch 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 torch
import torch.nn as nn
from torch.distributions import Categorical

# 是否使用GPU加速
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

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(nn.Module):
    def __init__(self, state_dim, action_dim, n_latent_var):
        super(ActorCritic, self).__init__()

        # 行动
        self.action_layer = nn.Sequential(
            # [b, 8] => [b, 64]
            nn.Linear(state_dim, n_latent_var),
            nn.Tanh(),  # 激活

            # [b, 64] => [b, 64]
            nn.Linear(n_latent_var, n_latent_var),
            nn.Tanh(),  # 激活

            # [b, 64] => [b, 4]
            nn.Linear(n_latent_var, action_dim),
            nn.Softmax(dim=-1)
        )

        # 评判
        self.value_layer = nn.Sequential(
            # [b, 8] => [8, 64]
            nn.Linear(state_dim, n_latent_var),
            nn.Tanh(),  # 激活

            # [b, 64] => [b, 64]
            nn.Linear(n_latent_var, n_latent_var),
            nn.Tanh(),

            # [b, 64] => [b, 1]
            nn.Linear(n_latent_var, 1)
        )

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

        raise NotImplementedError

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

        # 转成张量
        state = torch.from_numpy(state).float().to(device)

        # 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.item()

    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()

        # 评判
        state_value = self.value_layer(state)
        state_value = torch.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

class 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).to(device)
        self.policy_old = ActorCritic(state_dim, action_dim, n_latent_var).to(device)
        self.policy_old.load_state_dict(self.policy.state_dict())

        self.optimizer = torch.optim.Adam(self.policy.parameters(), lr=lr, betas=betas)  # 优化器
        self.MseLoss = nn.MSELoss()  # 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 = torch.tensor(rewards, dtype=torch.float32).to(device)
        rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-5)

        # 张量转换
        old_states = torch.stack(memory.states).to(device).detach()
        old_actions = torch.stack(memory.actions).to(device).detach()
        old_logprobs = torch.stack(memory.logprobs).to(device).detach()

        # 迭代优化 K 次:
        for _ in range(self.K_epochs):
            # 评估
            logprobs, state_values, dist_entropy = self.policy.evaluate(old_states, old_actions)

            # 计算ratios
            ratios = torch.exp(logprobs - old_logprobs.detach())

            # 计算损失
            advantages = rewards - state_values.detach()
            surr1 = ratios * advantages
            surr2 = torch.clamp(ratios, 1 - self.eps_clip, 1 + self.eps_clip) * advantages
            loss = -torch.min(surr1, surr2) + 0.5 * self.MseLoss(state_values, rewards) - 0.01 * dist_entropy

            # 梯度清零
            self.optimizer.zero_grad()

            # 反向传播
            loss.mean().backward()

            # 更新梯度
            self.optimizer.step()

        # 将新的权重赋值给旧policy
        self.policy_old.load_state_dict(self.policy.state_dict())

main

import gym
import torch
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）

        # 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）

            # 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
            torch.save(ppo.policy.state_dict(), './PPO_{}.pth'.format(env_name))

            # 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
Episode 3740 avg length: 296 reward: 174
Episode 3760 avg length: 267 reward: 124
Episode 3780 avg length: 273 reward: 148
Episode 3800 avg length: 275 reward: 147
Episode 3820 avg length: 276 reward: 145
Episode 3840 avg length: 283 reward: 151
Episode 3860 avg length: 275 reward: 142
Episode 3880 avg length: 290 reward: 142
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Episode 15520 avg length: 254 reward: 126
Episode 15540 avg length: 275 reward: 133
Episode 15560 avg length: 271 reward: 120
Episode 15580 avg length: 270 reward: 130
Episode 15600 avg length: 299 reward: 144
Episode 15620 avg length: 254 reward: 88
Episode 15640 avg length: 271 reward: 126
Episode 15660 avg length: 289 reward: 153
Episode 15680 avg length: 231 reward: 104
Episode 15700 avg length: 227 reward: 127
Episode 15720 avg length: 174 reward: 82
Episode 15740 avg length: 214 reward: 92
Episode 15760 avg length: 190 reward: 89
Episode 15780 avg length: 159 reward: 49
Episode 15800 avg length: 222 reward: 100
Episode 15820 avg length: 269 reward: 133
Episode 15840 avg length: 243 reward: 100
Episode 15860 avg length: 191 reward: 68
Episode 15880 avg length: 221 reward: 86
Episode 15900 avg length: 206 reward: 109
Episode 15920 avg length: 228 reward: 89
Episode 15940 avg length: 250 reward: 108
Episode 15960 avg length: 229 reward: 110
Episode 15980 avg length: 263 reward: 139
Episode 16000 avg length: 250 reward: 125
Episode 16020 avg length: 270 reward: 140
Episode 16040 avg length: 251 reward: 131
Episode 16060 avg length: 258 reward: 124
Episode 16080 avg length: 268 reward: 130
Episode 16100 avg length: 263 reward: 125
Episode 16120 avg length: 280 reward: 150
Episode 16140 avg length: 267 reward: 132
Episode 16160 avg length: 284 reward: 137
Episode 16180 avg length: 275 reward: 128
Episode 16200 avg length: 269 reward: 132
Episode 16220 avg length: 280 reward: 132
Episode 16240 avg length: 279 reward: 145
Episode 16260 avg length: 299 reward: 152
Episode 16280 avg length: 238 reward: 112
Episode 16300 avg length: 284 reward: 159
Episode 16320 avg length: 280 reward: 136
Episode 16340 avg length: 271 reward: 120
Episode 16360 avg length: 281 reward: 139
Episode 16380 avg length: 267 reward: 141
Episode 16400 avg length: 299 reward: 164
Episode 16420 avg length: 239 reward: 113
Episode 16440 avg length: 276 reward: 143
Episode 16460 avg length: 268 reward: 144
Episode 16480 avg length: 269 reward: 134
Episode 16500 avg length: 273 reward: 148
Episode 16520 avg length: 247 reward: 97
Episode 16540 avg length: 266 reward: 129
Episode 16560 avg length: 267 reward: 119
Episode 16580 avg length: 270 reward: 124
Episode 16600 avg length: 262 reward: 101
Episode 16620 avg length: 257 reward: 121
Episode 16640 avg length: 233 reward: 99
Episode 16660 avg length: 268 reward: 114
Episode 16680 avg length: 261 reward: 126
Episode 16700 avg length: 278 reward: 143
Episode 16720 avg length: 278 reward: 117
Episode 16740 avg length: 266 reward: 135
Episode 16760 avg length: 282 reward: 140
Episode 16780 avg length: 299 reward: 154
Episode 16800 avg length: 279 reward: 144
Episode 16820 avg length: 281 reward: 124
Episode 16840 avg length: 280 reward: 132
Episode 16860 avg length: 278 reward: 148
Episode 16880 avg length: 280 reward: 113
Episode 16900 avg length: 268 reward: 133
Episode 16920 avg length: 291 reward: 147
Episode 16940 avg length: 274 reward: 150
Episode 16960 avg length: 281 reward: 137
Episode 16980 avg length: 251 reward: 126
Episode 17000 avg length: 261 reward: 135
Episode 17020 avg length: 267 reward: 105
Episode 17040 avg length: 274 reward: 176
Episode 17060 avg length: 262 reward: 131
Episode 17080 avg length: 186 reward: 184
Episode 17100 avg length: 225 reward: 150
Episode 17120 avg length: 201 reward: 218
Episode 17140 avg length: 211 reward: 220
Episode 17160 avg length: 221 reward: 218
Episode 17180 avg length: 232 reward: 210
Episode 17200 avg length: 216 reward: 220
Episode 17220 avg length: 226 reward: 203
Episode 17240 avg length: 198 reward: 170
Episode 17260 avg length: 196 reward: 222
Episode 17280 avg length: 214 reward: 196
Episode 17300 avg length: 229 reward: 205
Episode 17320 avg length: 183 reward: 192
Episode 17340 avg length: 212 reward: 186
Episode 17360 avg length: 192 reward: 164
########## Solved! ##########

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