“IndexError: index 20 is out of bounds for axis 1 with size 20”是什么意思

编程问答 2022-05-24

问题描述

我在迷宫环境中进行q学习，但是，在初始阶段，它工作正常，但后来，我得到了以下信息 max_future_q = np.max(q_table[new_discrete_state]) 索引错误：索引 20 超出轴 1 的范围，大小为 20

我不明白这里的问题是什么 代码如下：

enter code here
import gym
import numpy as np
import gym_maze

env = gym.make("maze-v0")

LEARNING_RATE = 0.1

disCOUNT = 0.95
EPISODES = 25000
SHOW_EVERY = 3000

disCRETE_OS_SIZE = [20,20]
discrete_os_win_size = (env.observation_space.high - env.observation_space.low)/disCRETE_OS_SIZE

# Exploration settings
epsilon = 1  # not a constant,qoing to be decayed
START_EPSILON_DECAYING = 1
END_EPSILON_DECAYING = EPISODES//2
epsilon_decay_value = epsilon/(END_EPSILON_DECAYING - START_EPSILON_DECAYING)


q_table = np.random.uniform(low=-2,high=0,size=(disCRETE_OS_SIZE + [env.action_space.n]))


def get_discrete_state(state):
    discrete_state = (state - env.observation_space.low)/discrete_os_win_size
    return tuple(discrete_state.astype(np.int))  # we use this tuple to look up the 3 Q values for the available actions in the q-table


for episode in range(EPISODES):
    discrete_state = get_discrete_state(env.reset())
    done = False

    if episode % SHOW_EVERY == 0:
        render = True
        print(episode)
    else:
        render = False

    while not done:

        if np.random.random() > epsilon:
            # Get action from Q table
            action = np.argmax(q_table[discrete_state])
        else:
            # Get random action
            action = np.random.randint(0,env.action_space.n)


        new_state,reward,done,_ = env.step(action)

        new_discrete_state = get_discrete_state(new_state)

        if episode % SHOW_EVERY == 0:
            env.render()
        #new_q = (1 - LEARNING_RATE) * current_q + LEARNING_RATE * (reward + disCOUNT * max_future_q)

        # If simulation did not end yet after last step - update Q table
        if not done:

            # Maximum possible Q value in next step (for new state)
            max_future_q = np.max(q_table[new_discrete_state])

            # Current Q value (for current state and performed action)
            current_q = q_table[discrete_state + (action,)]

            # And here's our equation for a new Q value for current state and action
            new_q = (1 - LEARNING_RATE) * current_q + LEARNING_RATE * (reward + disCOUNT * max_future_q)

            # Update Q table with new Q value
            q_table[discrete_state + (action,)] = new_q


        # Simulation ended (for any reson) - if goal position is achived - update Q value with reward directly
        elif new_state[0] >= env.goal_position:
            #q_table[discrete_state + (action,)] = reward
            q_table[discrete_state + (action,)] = 0

        discrete_state = new_discrete_state

    # Decaying is being done every episode if episode number is within decaying range
    if END_EPSILON_DECAYING >= episode >= START_EPSILON_DECAYING:
        epsilon -= epsilon_decay_value


env.close()

解决方法

该错误意味着您尝试索引形状为 (n,20) axis 1 with size 20 和 20 的数组。例如 np.zeros((10,20))[:,20] 尝试验证您的 np 数组和索引的大小

,

索引越界错误意味着您正在尝试访问位于容器中不存在的索引处的项目。您不能在一行五人中选择第六个人。

与大多数编程语言一样，Python 是 0 索引的。这意味着容器中的第一个项目的索引为 0，而不是 1。因此，大小为 5 的容器中项目的索引为

0,1,2,3,4

如您所见，容器中最后一项的索引比容器的大小小 1。在python中，您可以使用

获取容器中最后一项的索引 
len(foo) - 1
mazepythonq-learningreinforcement-learning