pendulum-v0里的代码怎么改能适合Pendulum-v1环境
时间: 2024-04-29 17:23:40 浏览: 9
为了适应Pendulum-v1环境,需要将以下代码进行更改:
1. 引入新的环境
Pendulum-v1的环境名称为"Pendulum-v1",因此需要将原来的代码:
```python
import gym
env = gym.make('Pendulum-v0')
```
改为:
```python
import gym
env = gym.make('Pendulum-v1')
```
2. 修改状态空间和动作空间
Pendulum-v1的状态空间和动作空间与Pendulum-v0略有不同。具体来说,Pendulum-v1的状态空间为一个3维向量,代表了摆杆的角度、角速度和角加速度;动作空间为一个1维向量,代表了施加到摆杆上的扭矩力。
因此,需要将原来的代码:
```python
state_space = env.observation_space.shape[0]
action_space = env.action_space.shape[0]
```
修改为:
```python
state_space = env.observation_space.shape[0]
action_space = env.action_space.shape[0]
```
3. 修改奖励函数
Pendulum-v1的奖励函数与Pendulum-v0略有不同。具体来说,Pendulum-v1的奖励函数为:
$$
r(s,a,s') = -\theta^2 - 0.1\omega^2 - 0.001a^2
$$
其中,$\theta$表示摆杆的角度,$\omega$表示摆杆的角速度,$a$表示施加到摆杆上的扭矩力。
因此,需要将原来的代码:
```python
reward = -(theta ** 2 + 0.1 * theta_dt ** 2 + 0.001 * action ** 2)
```
修改为:
```python
reward = -(theta ** 2 + 0.1 * omega ** 2 + 0.001 * action ** 2)
```
完整代码如下所示:
```python
import gym
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
env = gym.make('Pendulum-v1')
state_space = env.observation_space.shape[0]
action_space = env.action_space.shape[0]
model = Sequential()
model.add(Dense(64, input_shape=(state_space,), activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(action_space, activation='linear'))
model.compile(loss='mse', optimizer=Adam())
gamma = 0.99
epsilon = 1.0
epsilon_decay = 0.995
epsilon_min = 0.01
episodes = 500
for episode in range(episodes):
state = env.reset()
done = False
score = 0
while not done:
if np.random.random() < epsilon:
action = env.action_space.sample()
else:
action = model.predict(state.reshape(1, state_space))[0]
next_state, reward, done, info = env.step(action)
theta, theta_dt = next_state[0], next_state[1]
score += reward
reward = -(theta ** 2 + 0.1 * theta_dt ** 2 + 0.001 * action ** 2)
target = reward + gamma * np.amax(model.predict(next_state.reshape(1, state_space))[0])
target_f = model.predict(state.reshape(1, state_space))
target_f[0][np.argmax(action)] = target
model.fit(state.reshape(1, state_space), target_f, epochs=1, verbose=0)
state = next_state
epsilon *= epsilon_decay
epsilon = max(epsilon_min, epsilon)
print("Episode: {}, Score: {:.2f}".format(episode, score))
```