使用gym训练parking-v0场景,使用DQN算法,给出示例代码,需要输出场景视频。
时间: 2024-06-09 19:11:18 浏览: 14
好的,我可以为您提供一个简单的示例代码,用DQN算法训练gym的parking-v0场景。请确保您已经安装了gym和tensorflow库。
首先,让我们导入所需的库:
```python
import gym
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from collections import deque
import random
import cv2
```
接下来,我们将定义我们的DQN网络。这里我们使用一个简单的神经网络,用于从每个状态中预测每个动作的Q值:
```python
class DQN:
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
self.memory = deque(maxlen=2000)
self.gamma = 0.95
self.epsilon = 1.0
self.epsilon_min = 0.01
self.epsilon_decay = 0.995
self.learning_rate = 0.001
self.model = self._build_model()
def _build_model(self):
model = Sequential()
model.add(Dense(32, input_dim=self.state_size, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
return model
def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def act(self, state):
if np.random.rand() <= self.epsilon:
return np.random.randint(self.action_size)
else:
return np.argmax(self.model.predict(state)[0])
def replay(self, batch_size):
minibatch = random.sample(self.memory, batch_size)
for state, action, reward, next_state, done in minibatch:
target = reward
if not done:
target = (reward + self.gamma * np.amax(self.model.predict(next_state)[0]))
target_f = self.model.predict(state)
target_f[0][action] = target
self.model.fit(state, target_f, epochs=1, verbose=0)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
def load(self, name):
self.model.load_weights(name)
def save(self, name):
self.model.save_weights(name)
```
现在,我们将定义我们的训练循环:
```python
state_size = 640 * 480 # 像素数量
action_size = 5 # 动作数量,包括左转,右转,前进,后退和停止
env = gym.make('parking-v0')
agent = DQN(state_size, action_size)
batch_size = 32
num_episodes = 100
for episode in range(num_episodes):
state = cv2.cvtColor(env.reset(), cv2.COLOR_BGR2GRAY) # 转换为灰度图像
state = cv2.resize(state, (640, 480)) # 调整大小
state = np.reshape(state, [1, state_size])
done = False
total_reward = 0
while not done:
action = agent.act(state)
next_state, reward, done, _ = env.step(action)
next_state = cv2.cvtColor(next_state, cv2.COLOR_BGR2GRAY)
next_state = cv2.resize(next_state, (640, 480))
next_state = np.reshape(next_state, [1, state_size])
agent.remember(state, action, reward, next_state, done)
state = next_state
total_reward += reward
if len(agent.memory) > batch_size:
agent.replay(batch_size)
print("Episode: {}, Score: {}".format(episode, total_reward))
env.close()
```
在训练结束后,我们可以使用以下代码保存我们的模型:
```python
agent.save("model.h5")
```
最后,我们可以使用以下代码测试我们的模型,并输出场景视频:
```python
agent.load("model.h5")
state = cv2.cvtColor(env.reset(), cv2.COLOR_BGR2GRAY)
state = cv2.resize(state, (640, 480))
state = np.reshape(state, [1, state_size])
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter('output.mp4', fourcc, 20.0, (640, 480))
done = False
while not done:
action = agent.act(state)
next_state, reward, done, _ = env.step(action)
next_state = cv2.cvtColor(next_state, cv2.COLOR_BGR2GRAY)
next_state = cv2.resize(next_state, (640, 480))
next_state = np.reshape(next_state, [1, state_size])
state = next_state
out.write(cv2.cvtColor(env.render(mode='rgb_array'), cv2.COLOR_RGB2BGR))
out.release()
env.close()
```
这将输出一个名为“output.mp4”的视频文件,显示我们的模型在parking-v0场景中的行为。
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