Python強化練習(xí)之PyTorch opp算法實現(xiàn)月球登陸器
概述
從今天開始我們會開啟一個新的篇章, 帶領(lǐng)大家來一起學(xué)習(xí) (卷進) 強化學(xué)習(xí) (Reinforcement Learning). 強化學(xué)習(xí)基于環(huán)境, 分析數(shù)據(jù)采取行動, 從而最大化未來收益.
強化學(xué)習(xí)算法種類
On-policy vs Off-policy:
- On-policy: 訓(xùn)練數(shù)據(jù)由當(dāng)前 agent 不斷與環(huán)境交互得到
- Off-policy: 訓(xùn)練的 agent 和與環(huán)境交互的 agent 不是同一個 agent, 即別人與環(huán)境交互為我提供訓(xùn)練數(shù)據(jù)
PPO 算法
PPO (Proximal Policy Optimization) 即近端策略優(yōu)化. PPO 是一種 on-policy 算法, 通過實現(xiàn)小批量更新, 解決了訓(xùn)練過程中新舊策略的變化差異過大導(dǎo)致不易學(xué)習(xí)的問題.
Actor-Critic 算法
Actor-Critic 算法共分為兩部分. 第一部分為策略函數(shù) Actor, 負責(zé)生成動作并與環(huán)境交互; 第二部分為價值函數(shù), 負責(zé)評估 Actor 的表現(xiàn).
Gym
Gym 是一個強化學(xué)習(xí)會經(jīng)常用到的包. Gym 里收集了很多游戲的環(huán)境. 下面我們就會用 LunarLander-v2 來實現(xiàn)一個自動版的 “阿波羅登月”.
安裝:
pip install gym
如果遇到報錯:
AttributeError: module 'gym.envs.box2d' has no attribute 'LunarLander'
解決辦法:
pip install gym[box2d]
LunarLander-v2
LunarLander-v2 是一個月球登陸器. 著陸平臺位于坐標(biāo) (0, 0). 坐標(biāo)是狀態(tài)向量的前兩個數(shù)字, 從屏幕頂部移動到著陸臺和零速度的獎勵大約是 100 到 140分. 如果著陸器墜毀或停止, 則回合結(jié)束, 獲得額外的 -100 或 +100點. 每腳接地為 +10, 點火主機每幀 -0.3分, 正解為200分.
啟動登陸器
代碼:
import gym
# 創(chuàng)建環(huán)境
env = gym.make("LunarLander-v2")
# 重置環(huán)境
env.reset()
# 啟動
for i in range(180):
# 渲染環(huán)境
env.render()
# 隨機移動
observation, reward, done, info = env.step(env.action_space.sample())
if i % 10 == 0:
# 調(diào)試輸出
print("觀察:", observation)
print("得分:", reward)
輸出結(jié)果:
觀察: [ 0.00861025 1.4061487 0.42930993 -0.11858992 -0.00789343 -0.05729095
0. 0. ]
得分: 0.4097546298543773
觀察: [ 0.04917412 1.3876126 0.41002613 -0.13066985 -0.06578191 -0.12604967
0. 0. ]
得分: -1.0858669952763478
觀察: [ 0.08917055 1.3429415 0.43598312 -0.2890789 -0.17471936 -0.23913136
0. 0. ]
得分: -2.9339827504803666
觀察: [ 0.1326253 1.2450166 0.44708318 -0.5567949 -0.32039645 -0.28250334
0. 0. ]
得分: -2.2779730990326357
觀察: [ 0.18323365 1.1110108 0.615291 -0.61922276 -0.43743232 -0.2921057
0. 0. ]
得分: -3.107298313736037
觀察: [ 0.24544087 0.94960684 0.66677517 -0.7835077 -0.5929364 -0.2968613
0. 0. ]
得分: -0.5472611013563438
觀察: [ 0.3148238 0.75122666 0.7238519 -0.98458177 -0.72915816 -0.26130882
0. 0. ]
得分: -2.5665300894414416
觀察: [ 0.38628978 0.49828076 0.74157137 -1.2624744 -0.85754734 -0.37227553
0. 0. ]
得分: -3.2562193227533087
觀察: [ 0.46820658 0.18855602 0.92624503 -1.4677961 -1.08614 -0.4508995
0. 0. ]
得分: -4.017106927961208
觀察: [ 0.57930076 -0.09440845 1.4345247 -0.693939 -2.0783656 -5.4039164
1. 0. ]
得分: -100
觀察: [ 0.7383894 -0.08930686 1.4662493 -0.13461255 -3.653495 -3.109081
0. 0. ]
得分: -100
觀察: [ 0.859124 -0.08471288 0.9377837 0.21408719 -3.8998525 0.10151418
0. 0. ]
得分: -100
觀察: [ 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
觀察: [ 0.9879366 -0.04012476 0.33624884 0.08859511 -4.253908 -1.0233303
0. 0. ]
得分: -100
觀察: [ 1.0056045 -0.03840658 0.0733737 0.01812508 -4.6796274 -0.6103991
0. 0. ]
得分: -100
觀察: [ 1.0112988 -0.03921754 0.07890484 -0.00624387 -4.845023 -0.17111658
0. 0. ]
得分: -100
觀察: [ 1.0234139 -0.04488504 0.15701209 -0.0331554 -4.829875 0.07602684
0. 0. ]
得分: -100
觀察: [ 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 算法實現(xiàn)月球登錄器
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 = [] # 行動(共4種)
self.states = [] # 狀態(tài), 由8個數(shù)字組成
self.logprobs = [] # 概率
self.rewards = [] # 獎勵
self.is_terminals = [] # 游戲是否結(jié)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__()
# 行動
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)
)
# 評判
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):
"""前向傳播, 由act替代"""
raise NotImplementedError
def act(self, state, memory):
"""計算行動"""
# 轉(zhuǎn)成張量
state = torch.from_numpy(state).float().to(device)
# 計算4個方向概率
action_probs = self.action_layer(state)
# 通過最大概率計算最終行動方向
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))
# 返回行動
return action.item()
def evaluate(self, state, action):
"""
評估
:param state: 狀態(tài), 2000個一組, 形狀為 [2000, 8]
:param action: 行動, 2000個一組, 形狀為 [2000]
:return:
"""
# 計算行動概率
action_probs = self.action_layer(state)
dist = Categorical(action_probs) # 轉(zhuǎn)換成類別分布
# 計算概率密度, log(概率)
action_logprobs = dist.log_prob(action)
# 計算熵
dist_entropy = dist.entropy()
# 評判
state_value = self.value_layer(state)
state_value = torch.squeeze(state_value) # [2000, 1] => [2000]
# 返回行動概率密度, 評判值, 行動概率熵
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 # 學(xué)習(xí)率
self.betas = betas # betas
self.gamma = gamma # gamma
self.eps_clip = eps_clip # 裁剪, 限制值范圍
self.K_epochs = K_epochs # 迭代次數(shù)
# 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) # 優(yōu)化器
self.MseLoss = nn.MSELoss() # 損失函數(shù)
def update(self, memory):
"""更新梯度"""
# 蒙特卡羅預(yù)測狀態(tài)回報
rewards = []
discounted_reward = 0
for reward, is_terminal in zip(reversed(memory.rewards), reversed(memory.is_terminals)):
# 回合結(jié)束
if is_terminal:
discounted_reward = 0
# 更新削減獎勵(當(dāng)前狀態(tài)獎勵 + 0.99*上一狀態(tài)獎勵
discounted_reward = reward + (self.gamma * discounted_reward)
# 首插入
rewards.insert(0, discounted_reward)
# 標(biāo)準化獎勵
rewards = torch.tensor(rewards, dtype=torch.float32).to(device)
rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-5)
# 張量轉(zhuǎn)換
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()
# 迭代優(yōu)化 K 次:
for _ in range(self.K_epochs):
# 評估
logprobs, state_values, dist_entropy = self.policy.evaluate(old_states, old_actions)
# 計算ratios
ratios = torch.exp(logprobs - old_logprobs.detach())
# 計算損失
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()
# 反向傳播
loss.mean().backward()
# 更新梯度
self.optimizer.step()
# 將新的權(quán)重賦值給舊policy
self.policy_old.load_state_dict(self.policy.state_dict())
main
import gym
import torch
from PPO import Memory, PPO
############## 超參數(shù) ##############
env_name = "LunarLander-v2" # 游戲名字
env = gym.make(env_name)
state_dim = 8 # 狀態(tài)維度
action_dim = 4 # 行動維度
render = False # 可視化
solved_reward = 230 # 停止循環(huán)條件 (獎勵 > 230)
log_interval = 20 # print avg reward in the interval
max_episodes = 50000 # 最大迭代次數(shù)
max_timesteps = 300 # 最大單次游戲步數(shù)
n_latent_var = 64 # 全連接隱層維度
update_timestep = 2000 # 每2000步policy更新一次
lr = 0.002 # 學(xué)習(xí)率
betas = (0.9, 0.999) # betas
gamma = 0.99 # gamma
K_epochs = 4 # policy迭代更新次數(shù)
eps_clip = 0.2 # PPO 限幅
#############################################
def main():
# 實例化
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
# 訓(xùn)練
for i_episode in range(1, max_episodes + 1):
# 環(huán)境初始化
state = env.reset() # 初始化（重新玩）
# 迭代
for t in range(max_timesteps):
timestep += 1
# 用舊policy得到行動
action = ppo.policy_old.act(state, memory)
# 行動
state, reward, done, _ = env.step(action) # 得到（新的狀態(tài)，獎勵，是否終止，額外的調(diào)試信息）
# 更新memory(獎勵/游戲是否結(jié)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()
# 累計步數(shù)清零
timestep = 0
# 累加
total_reward += reward
# 可視化
if render:
env.render()
# 如果游戲結(jié)束, 退出
if done:
break
# 游戲步長
total_length += t
# 如果達到要求(230分), 退出循環(huán)
if total_reward >= (log_interval * solved_reward):
print("########## Solved! ##########")
# 保存模型
torch.save(ppo.policy.state_dict(), './PPO_{}.pth'.format(env_name))
# 退出循環(huán)
break
# 輸出log, 每20次迭代
if i_episode % log_interval == 0:
# 求20次迭代平均時長/收益
avg_length = int(total_length / log_interval)
running_reward = int(total_reward / log_interval)
# 調(diào)試輸出
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()
輸出結(jié)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
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Episode 15180 avg length: 223 reward: 111
Episode 15200 avg length: 243 reward: 134
Episode 15220 avg length: 269 reward: 138
Episode 15240 avg length: 251 reward: 127
Episode 15260 avg length: 289 reward: 157
Episode 15280 avg length: 229 reward: 107
Episode 15300 avg length: 277 reward: 143
Episode 15320 avg length: 288 reward: 154
Episode 15340 avg length: 289 reward: 149
Episode 15360 avg length: 288 reward: 145
Episode 15380 avg length: 260 reward: 134
Episode 15400 avg length: 246 reward: 126
Episode 15420 avg length: 244 reward: 132
Episode 15440 avg length: 272 reward: 129
Episode 15460 avg length: 267 reward: 134
Episode 15480 avg length: 263 reward: 135
Episode 15500 avg length: 280 reward: 141
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! ##########
到此這篇關(guān)于Python強化練習(xí)之PyTorch opp算法實現(xiàn)月球登陸器的文章就介紹到這了,更多相關(guān)Python OPP內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！
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