我假设输入3是转发到模型网络的状态大小，300是模型网络产生的输出大小。
现在，您想为（number_of_rays）中的每个元素调用模型网络的单独示例吗？如果是，那么获取数组中每个元素的梯度的一种方法是为分配给数组元素的模型网络的每个示例分配单独的优化器。
下面是我的代码：

class Environment():
    def __init__(self, N, input_dims, output_dims, lr, gamma):
        self.number_of_rays = N
        self.input_dims = input_dims
        self.output_dims = output_dims
        # assign to each ray a neural network
        self.rays = [YourModel(input_dims, output_dims, gamma) for i in range(N)]
        self.optimizer = []
        for i in range(N):
            optim = T.optim.Adam(self.rays[i].parameters(), lr=lr, betas=(0.92, 0.999))
            self.optimizer.append(optim)

    def run(self):
        # here you do your stuff 
        # ...

        # now update the networks
        for i in range(self.number_of_rays):
            prediction = self.rays[i](observation) # feed your input state to the model
            loss = loss_fn(prediction) # compute loss
            self.optimizer[i].zero_grad() # reset the gradients of model parameters
            loss.backward() # backpropagate the prediction loss
            self.optimizer[i].step() # adjust the parameters by the gradients collected in the backward pass