GAIL on N/W reconfiguration

Classes and Functions

phy_train_gail.evaluate_policy(env, model)

Evaluating the learned policy by running experiments in the environment

Parameters

  • env (Gym.Env) – The Open DSS RL environment

  • model (torch.nn.Module) – Trained policy network model

Returns

average episode length, average reward

Return type

float

phy_train_gail.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([])
phy_train_gail.train_and_evaluate(env, gail_train_lens, policy_net_train_len, exp_trajectory_len)

For different combination of GAIL training length evaluate the learned policy and saves the reward network.

Parameters

  • env (Gym.Env) – The Open DSS RL environment

  • exp_tajectory_len (int) – List of the number of expert demonstrations steps considered for AIRL training

  • policy_net_train_len (int) – Samples considered for training the policy network fed in the generator network as initial policy

  • gail_train_lens (list) – List of combination of GAIL training length evaluated

Returns

Nothing

Return type

None