DQN网络代码调用env.reset()后存储观测信息报错问题ValueError: setting an array element with a sequence.解决处理

在强化学习DQN网络代码实现过程中，针对gym环境中的LunarLander-v2模拟游戏进行学习任务。我的部分代码如下，首先是Agent的状态存储过程：

def __init__(self,gamma,epsilon,lr,input_dims,batch_size,n_actions,    
        # ....此前省略 ....
        self.state_memory = np.zeros((self.mem_size,*input_dims),dtype=np.float32)
        self.new_state_memory = np.zeros((self.mem_size,*input_dims),dtype=np.float32)
        
        self.action_memory = np.zeros(self.mem_size,dtype=np.int32)
        self.reward_memory = np.zeros(self.mem_size,dtype=np.float32)
        self.terminal_memory = np.zeros(self.mem_size,dtype=np.bool)
    def store_transitons(self,state,action,reward,state_,done):
        index = self.mem_cntr % self.mem_size
        self.state_memory[index] = state
        self.action_memory[index] = action
        self.new_state_memory[index] = state_
        self.terminal_memory[index] = done
        self.reward_memory[index] = reward

下面是main代码中的学习循环过程：

if __name__ == '__main__':
    env = gym.make('LunarLander-v2')
    agent = Agent(gamma= 0.99 ,epsilon=1.0 , batch_size=64, n_actions=4,
    eps_end= 0.01 ,input_dims=[8], lr=0.003)
    scores,eps_history = [],[]
    n_games = 10
    for i in range(n_games):
        score = 0
        done = False
        observation  = env.reset() 
        while not done:
            action = agent.choose_action(observation)
            observation_, reward, done, info, __ = env.step(action)
            score += reward
            agent.store_transitons(observation,action,reward,observation_,done)
            agent.learn()
            observation = observation_
        scores.append(score)
        eps_history.append(agent.epsilon)
        # .................#

执行上述代码后报错：

Traceback (most recent call last):
  File ".\main_Lunar_lander.py", line 21, in <module>
    agent.store_transitons(observation,action,reward,observation_,done)
  File "D:\College\Projects\Person_Research\DQN_From_Yotube\DQN.py", line 59, in store_transitons
    self.state_memory[index] = state
ValueError: setting an array element with a sequence. The requested array would exceed the maximum number of dimension of 1.

报错信息提示为数据维度不对应，也即最初通过observation = env.reset()得到的的变量observation类型与Agent存储时的np.float32类型不匹配，而gym官方文档对reset()函数的描述为：
因此怀疑返回的observation有问题，于是通过print('observation:',observation)打印观察返回的observation，显示如下：
reset()函数返回的是一个array类型以及其中数据的type！
因此需要将observation指定为真正需要的array信息即可,observation,__ = env.reset()

    for i in range(n_games):
        score = 0
        done = False
        observation,__  = env.reset() 
        while not done:
            action = agent.choose_action(observation)
            observation_, reward, done, info, __ = env.step(action)
            score += reward
            agent.store_transitons(observation,action,reward,observation_,done)
            agent.learn()
            observation = observation_

此后再次运行，网络就可以正常工作了：