Reward Network Visualization for Critical Load Restoration

Classes and Functions

visualize_reward_net_physical.Plot3D(X, Y, V)

This function is used for surface plots X, Y and V indicates value for the 3 different axis

visualize_reward_net_physical.get_reward(path)

Function to obtain the reward value from the learned reward network

visualize_reward_net_physical.plot_reward_network(model)

Function to plot the reward network for the MDP in the cyber environment

visualize_reward_net_physical.tensor(data, *, dtype=None, device=None, requires_grad=False, pin_memory=False) → Tensor

Constructs a tensor with data.

Warning

torch.tensor() always copies data. If you have a Tensor data and want to avoid a copy, use torch.Tensor.requires_grad_() or torch.Tensor.detach(). If you have a NumPy ndarray and want to avoid a copy, use torch.as_tensor().

Warning

When data is a tensor x, torch.tensor() reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Therefore torch.tensor(x) is equivalent to x.clone().detach() and torch.tensor(x, requires_grad=True) is equivalent to x.clone().detach().requires_grad_(True). The equivalents using clone() and detach() are recommended.

Args:

data (array_like): Initial data for the tensor. Can be a list, tuple,

NumPy ndarray, scalar, and other types.

Keyword args:

dtype (torch.dtype, optional): the desired data type of returned tensor.

Default: if None, infers data type from data.

device (torch.device, optional): the desired device of returned tensor.

Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

requires_grad (bool, optional): If autograd should record operations on the

returned tensor. Default: False.

pin_memory (bool, optional): If set, returned tensor would be allocated in

the pinned memory. Works only for CPU tensors. Default: False.

Example:

>>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
tensor([[ 0.1000,  1.2000],
        [ 2.2000,  3.1000],
        [ 4.9000,  5.2000]])

>>> torch.tensor([0, 1])  # Type inference on data
tensor([ 0,  1])

>>> torch.tensor([[0.11111, 0.222222, 0.3333333]],
...              dtype=torch.float64,
...              device=torch.device('cuda:0'))  # creates a torch.cuda.DoubleTensor
tensor([[ 0.1111,  0.2222,  0.3333]], dtype=torch.float64, device='cuda:0')

>>> torch.tensor(3.14159)  # Create a scalar (zero-dimensional tensor)
tensor(3.1416)

>>> torch.tensor([])  # Create an empty tensor (of size (0,))
tensor([])