Reinforcement Learning
This commit is contained in:
e2hang
8
Pytorch/Project/CartPole/.idea/CartPole.iml
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6
Pytorch/Project/CartPole/.idea/inspectionProfiles/profiles_settings.xml
generated
Normal file
6
Pytorch/Project/CartPole/.idea/inspectionProfiles/profiles_settings.xml
generated
Normal file
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<component name="InspectionProjectProfileManager">
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<settings>
|
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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</settings>
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7
Pytorch/Project/CartPole/.idea/misc.xml
generated
Normal file
7
Pytorch/Project/CartPole/.idea/misc.xml
generated
Normal file
@@ -0,0 +1,7 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="mjai" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="eflow" project-jdk-type="Python SDK" />
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</project>
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8
Pytorch/Project/CartPole/.idea/modules.xml
generated
Normal file
8
Pytorch/Project/CartPole/.idea/modules.xml
generated
Normal file
@@ -0,0 +1,8 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/CartPole.iml" filepath="$PROJECT_DIR$/.idea/CartPole.iml" />
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</modules>
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79
Pytorch/Project/CartPole/.idea/workspace.xml
generated
Normal file
79
Pytorch/Project/CartPole/.idea/workspace.xml
generated
Normal file
@@ -0,0 +1,79 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="AutoImportSettings">
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<option name="autoReloadType" value="SELECTIVE" />
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<component name="ChangeListManager">
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<list default="true" id="9860575b-1edf-47fa-be34-791024b3218c" name="更改" comment="" />
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<option name="SHOW_DIALOG" value="false" />
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<option name="HIGHLIGHT_CONFLICTS" value="true" />
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<option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
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<option name="LAST_RESOLUTION" value="IGNORE" />
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<component name="FileTemplateManagerImpl">
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<option name="RECENT_TEMPLATES">
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<list>
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<option name="showLibraryContents" value="true" />
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<component name="PropertiesComponent"><![CDATA[{
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"Python.AddBuffer.executor": "Run",
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"Python.NoBuffer.executor": "Run",
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"Python.main.executor": "Run",
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"RunOnceActivity.ShowReadmeOnStart": "true",
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"settings.editor.selected.configurable": "com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable"
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<configuration name="NoBuffer" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
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<option name="PARENT_ENVS" value="true" />
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<env name="PYTHONUNBUFFERED" value="1" />
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<option name="number" value="Default" />
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<option name="presentableId" value="Default" />
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</project>
|
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114
Pytorch/Project/CartPole/AddBuffer.py
Normal file
114
Pytorch/Project/CartPole/AddBuffer.py
Normal file
@@ -0,0 +1,114 @@
|
||||
from collections import deque
|
||||
import numpy as np
|
||||
import random
|
||||
import torch
|
||||
import torch.optim as optim
|
||||
import torch.nn as nn
|
||||
import gymnasium as gym
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class DQN(nn.Module):
|
||||
def __init__(self, state_size, action_size):
|
||||
super(DQN, self).__init__()
|
||||
self.l1 = nn.Linear(state_size, 128)
|
||||
self.l2 = nn.Linear(128, 32)
|
||||
self.l4 = nn.Linear(32, action_size)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.l1(x))
|
||||
x = F.relu(self.l2(x))
|
||||
x = self.l4(x)
|
||||
return x
|
||||
|
||||
lr = 0.001
|
||||
gamma = 0.99
|
||||
epsilon = 1.0
|
||||
memory = deque(maxlen=1000000)
|
||||
batch_size = 64
|
||||
epsilon_decay = 0.995
|
||||
epsilon_min = 0.01
|
||||
|
||||
env = gym.make('CartPole-v1')
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
|
||||
|
||||
ez = DQN(state_size, action_size)
|
||||
optimizer = optim.Adam(ez.parameters(), lr=lr)
|
||||
criterion = nn.MSELoss()
|
||||
|
||||
def replay():
|
||||
if(len(memory) < batch_size):
|
||||
return
|
||||
|
||||
#随机选一组64个训练
|
||||
batch = random.sample(memory, batch_size)
|
||||
states, actions, rewards, next_states, dones = zip(*batch)
|
||||
states = torch.from_numpy(np.stack(states)) # 直接把 batch 堆成 array 再转 tensor
|
||||
next_states = torch.from_numpy(np.stack(next_states))
|
||||
rewards = torch.from_numpy(np.array(rewards, dtype=np.float32))
|
||||
actions = torch.from_numpy(np.array(actions, dtype=np.int64))
|
||||
dones = torch.from_numpy(np.array(dones, dtype=np.float32))
|
||||
|
||||
q_values = ez(states).gather(1, actions.unsqueeze(1)).squeeze()
|
||||
next_q_values = ez(next_states).max(1)[0]
|
||||
target = rewards + gamma * next_q_values * (1 - dones)
|
||||
|
||||
loss = criterion(q_values, target.detach())
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
for e in range(350):
|
||||
state, _ = env.reset()
|
||||
done = False
|
||||
total_reward = 0
|
||||
while not done:
|
||||
if random.random() < epsilon:
|
||||
action = env.action_space.sample()
|
||||
else:
|
||||
action = ez(torch.tensor(state, dtype=torch.float32)).argmax().item()
|
||||
|
||||
next_state, reward, terminated, truncated, _ = env.step(action)
|
||||
done = terminated or truncated
|
||||
# 在存储到 memory 时：
|
||||
memory.append((np.array(state, dtype=np.float32),
|
||||
action,
|
||||
reward,
|
||||
np.array(next_state, dtype=np.float32),
|
||||
done))
|
||||
state = next_state
|
||||
total_reward += reward
|
||||
replay()
|
||||
|
||||
epsilon = max(epsilon * epsilon_decay, epsilon_min)
|
||||
print(f"Episode {e + 1}: Reward = {total_reward}")
|
||||
|
||||
torch.save(ez.state_dict(), "cartpole_dqn.pth")
|
||||
print("Model saved to cartpole_dqn.pth")
|
||||
|
||||
# 测试函数
|
||||
def test_agent(env, model, episodes=10):
|
||||
env = gym.make('CartPole-v1', render_mode='human')
|
||||
total_rewards = []
|
||||
for ep in range(episodes):
|
||||
state, _ = env.reset()
|
||||
done = False
|
||||
total_reward = 0.0
|
||||
while not done:
|
||||
# 关闭梯度计算，加速
|
||||
with torch.no_grad():
|
||||
action = model(torch.tensor(state, dtype=torch.float32)).argmax().item()
|
||||
next_state, reward, terminated, truncated, _ = env.step(action)
|
||||
done = terminated or truncated
|
||||
state = next_state
|
||||
total_reward += reward
|
||||
total_rewards.append(total_reward)
|
||||
print(f"Test Episode {ep+1}: reward = {total_reward}")
|
||||
|
||||
avg_reward = sum(total_rewards) / episodes
|
||||
print(f"Average reward over {episodes} episodes: {avg_reward}")
|
||||
|
||||
# 调用测试
|
||||
test_agent(env, ez, episodes=10)
|
||||
114
Pytorch/Project/CartPole/NoBuffer.py
Normal file
114
Pytorch/Project/CartPole/NoBuffer.py
Normal file
@@ -0,0 +1,114 @@
|
||||
import gymnasium as gym
|
||||
import random
|
||||
import torch
|
||||
import torch.optim as optim
|
||||
import torch.nn.functional as F
|
||||
import torch.nn as nn
|
||||
import time
|
||||
|
||||
# 创建环境
|
||||
#env = gym.make("CartPole-v1", render_mode="human") # human模式会用pyglet显示窗口
|
||||
env = gym.make('CartPole-v1')
|
||||
# 重置环境
|
||||
observation, info = env.reset()
|
||||
print("初始观察值:", observation)
|
||||
|
||||
#记住DQN训练的是Q*，输出的也是Q*，而不是动作，动作要根据Q*判断并反馈
|
||||
class DQN(nn.Module):
|
||||
def __init__(self, state_size, action_size):
|
||||
super(DQN, self).__init__()
|
||||
self.l1 = nn.Linear(state_size, 128)
|
||||
self.l3 = nn.Linear(128, 64)
|
||||
self.l4 = nn.Linear(64, 32)
|
||||
self.l5 = nn.Linear(32, action_size)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.l1(x))
|
||||
x = F.relu(self.l3(x))
|
||||
x = F.relu(self.l4(x))
|
||||
x = self.l5(x)
|
||||
return x
|
||||
|
||||
|
||||
#定义参数
|
||||
epsilon = 0.95
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
gamma = 0.99
|
||||
lrs = 0.005
|
||||
epsilon_decay = 0.995
|
||||
epsilon_min = 0.01
|
||||
batch_size = 64
|
||||
#memory = deque(maxlen=10000)
|
||||
num_episodes = 500
|
||||
|
||||
#初始化
|
||||
ez = DQN(state_size, action_size)
|
||||
optimizer = optim.Adam(ez.parameters(), lr = lrs)
|
||||
criterion = nn.MSELoss()
|
||||
|
||||
#训练
|
||||
for i in range(500):
|
||||
state, _ = env.reset() # gym >=0.26 返回 (obs, info)
|
||||
total_reward = 0.0
|
||||
done = False
|
||||
|
||||
while not done:
|
||||
# ε-greedy 选择动作
|
||||
if random.random() < epsilon:
|
||||
action = env.action_space.sample()
|
||||
else:
|
||||
action = ez(torch.tensor(state, dtype=torch.float32)).argmax().item()
|
||||
|
||||
# 与环境交互
|
||||
next_state, reward, terminated, truncated, _ = env.step(action)
|
||||
done = terminated or truncated
|
||||
total_reward += reward
|
||||
|
||||
# 计算 Q(s, a)
|
||||
q_values = ez(torch.tensor(state, dtype=torch.float32))
|
||||
now_Q = q_values[action]
|
||||
|
||||
# 计算 target
|
||||
next_q_value = ez(torch.tensor(next_state, dtype=torch.float32)).max().item()
|
||||
target_Q = reward + gamma * next_q_value * (0 if done else 1)
|
||||
|
||||
# 计算损失
|
||||
target_Q = torch.tensor(target_Q, dtype=torch.float32)
|
||||
loss = criterion(now_Q, target_Q)
|
||||
|
||||
# 更新网络
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# 状态更新
|
||||
state = next_state
|
||||
|
||||
print(f"Episode {i}, total_reward = {total_reward}")
|
||||
|
||||
# 测试函数
|
||||
def test_agent(env, model, episodes=10):
|
||||
total_rewards = []
|
||||
for ep in range(episodes):
|
||||
state, _ = env.reset()
|
||||
done = False
|
||||
total_reward = 0.0
|
||||
while not done:
|
||||
# 关闭梯度计算，加速
|
||||
with torch.no_grad():
|
||||
action = model(torch.tensor(state, dtype=torch.float32)).argmax().item()
|
||||
next_state, reward, terminated, truncated, _ = env.step(action)
|
||||
done = terminated or truncated
|
||||
state = next_state
|
||||
total_reward += reward
|
||||
total_rewards.append(total_reward)
|
||||
print(f"Test Episode {ep+1}: reward = {total_reward}")
|
||||
|
||||
avg_reward = sum(total_rewards) / episodes
|
||||
print(f"Average reward over {episodes} episodes: {avg_reward}")
|
||||
|
||||
# 调用测试
|
||||
test_agent(env, ez, episodes=10)
|
||||
|
||||
#最佳数据：平均150
|
||||
22
Pytorch/Project/CartPole/TestEnv.py
Normal file
22
Pytorch/Project/CartPole/TestEnv.py
Normal file
@@ -0,0 +1,22 @@
|
||||
import gymnasium as gym
|
||||
import time
|
||||
|
||||
# 创建环境
|
||||
env = gym.make("CartPole-v1", render_mode="human") # human模式会用pyglet显示窗口
|
||||
|
||||
# 重置环境
|
||||
observation, info = env.reset()
|
||||
print("初始观察值:", observation)
|
||||
print("<UNK>:", info)
|
||||
|
||||
# 随机动作走几步
|
||||
for step in range(100):
|
||||
observation, reward, terminated, truncated, info = env.step(1)
|
||||
print(f"Step {step+1}: observation={observation}, reward={reward}, done={terminated}")
|
||||
|
||||
if terminated or truncated:
|
||||
observation, info = env.reset()
|
||||
|
||||
time.sleep(1)
|
||||
# 关闭环境
|
||||
env.close()
|
||||
0
Pytorch/Project/CartPole/TestPthModule.py
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0
Pytorch/Project/CartPole/TestPthModule.py
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Pytorch/Project/CartPole/cartpole_dqn_success.pth
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Pytorch/Project/CartPole/cartpole_dqn_success.pth
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Pytorch/Project/ez0/HWCNN.png
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Reference in New Issue
Block a user