目录
- ppo 算法
- actor-critic 算法
- gym
- lunarlander-v2
- ppo 算法实现月球登录器
- ppo
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
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)
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
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
episode 3900 avg length: 290 reward: 154
episode 3920 avg length: 283 reward: 141
episode 3940 avg length: 273 reward: 145
episode 3960 avg length: 290 reward: 161
episode 3980 avg length: 268 reward: 145
episode 4000 avg length: 270 reward: 142
episode 4020 avg length: 283 reward: 156
episode 4040 avg length: 283 reward: 149
episode 4060 avg length: 299 reward: 172
episode 4080 avg length: 292 reward: 158
episode 4100 avg length: 274 reward: 143
episode 4120 avg length: 299 reward: 163
episode 4140 avg length: 290 reward: 153
episode 4160 avg length: 299 reward: 165
episode 4180 avg length: 290 reward: 160
episode 4200 avg length: 299 reward: 157
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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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