Pytorch：提供的尺寸数量（1）必须大于或等于张量中的维数（3）

Question

我正在尝试为我的 dqn 代理使用优先重播缓冲区。我遇到的问题如下。

我有一个具有 (40, 40, 1) 状态表示的世界。当我尝试向缓冲区添加过渡时，它给了我：

RuntimeError: expand(torch.DoubleTensor{[40, 40, 1]}, size=[3]): the number of sizes provided (1) must be greater or equal to the number of dimensions in the tensor (3)

优先重播缓冲区代码：

class PrioritizedReplayBuffer:
    def __init__(self, state_size=3, action_size=1, buffer_size=10000, eps=1e-2, alpha=0.1, beta=0.1):
        self.tree = SumTree(size=buffer_size)

        # PER params
        self.eps = eps 
        self.alpha = alpha
        self.beta = beta
        self.max_priority = eps

        # transition: state, action, reward, next_state, done
        self.state = torch.empty(buffer_size, state_size, dtype=torch.float)
        self.action = torch.empty(buffer_size, action_size, dtype=torch.float)
        self.reward = torch.empty(buffer_size, dtype=torch.float)
        self.next_state = torch.empty(buffer_size, state_size, dtype=torch.float)
        self.done = torch.empty(buffer_size, dtype=torch.int)

        self.count = 0
        self.real_size = 0
        self.size = buffer_size

    def add(self, transition):
        state, action, reward, next_state, done = transition

        # store transition index with maximum priority in sum tree
        self.tree.add(self.max_priority, self.count)

        # store transition in the buffer
        self.state[self.count] = torch.as_tensor(state)
        self.action[self.count] = torch.as_tensor(action)
        self.reward[self.count] = torch.as_tensor(reward)
        self.next_state[self.count] = torch.as_tensor(next_state)
        self.done[self.count] = torch.as_tensor(done)

        # update counters
        self.count = (self.count + 1) % self.size
        self.real_size = min(self.size, self.real_size + 1)

任何帮助将不胜感激。 谢谢