用python写用Q-learning算法控制倒立摆的代码
时间: 2023-06-03 16:06:56 浏览: 95
好的,这是一个技术问题,我可以回答。你可以参考下面的代码:
```python
import gym
import numpy as np
env = gym.make('InvertedPendulum-v2')
# Initialize Q table
num_states = env.observation_space.shape[0]
num_actions = env.action_space.shape[0]
q_table = np.zeros((num_states, num_actions))
# Set hyperparameters
alpha = 0.1
gamma = 0.99
epsilon = 1.0
epsilon_decay = 0.999
# Run episodes
for i_episode in range(10000):
state = env.reset()
done = False
while not done:
# Choose action
if np.random.random() > epsilon:
action = np.argmax(q_table[state])
else:
action = env.action_space.sample()
# Take action and observe new state and reward
next_state, reward, done, _ = env.step(action)
# Update Q-table
td_target = reward + gamma * np.max(q_table[next_state])
td_error = td_target - q_table[state][action]
q_table[state][action] += alpha * td_error
# Update state
state = next_state
# Decay epsilon
epsilon *= epsilon_decay
# Print episode number and total reward
if i_episode % 100 == 0:
total_reward = 0
state = env.reset()
done = False
while not done:
action = np.argmax(q_table[state])
next_state, reward, done, _ = env.step(action)
total_reward += reward
state = next_state
print(f'Episode {i_episode}: total reward = {total_reward}')
```
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