mohmdelsayed

import os, pickle, argparse
import torch
import numpy as np
import torch.nn as nn
import gymnasium as gym
import torch.nn.functional as F
from torch.distributions import Normal
from optim import ObGD as Optimizer
from time_wrapper import AddTimeInfo
from normalization_wrappers import NormalizeObservation, ScaleReward

mohmdelsayed

import os, pickle, argparse
import torch
import numpy as np
import torch.nn as nn
import gymnasium as gym
import torch.nn.functional as F
from torch.distributions import Normal
from optim import ObGD as Optimizer
from time_wrapper import AddTimeInfo
from normalization_wrappers import NormalizeObservation, ScaleReward

mohmdelsayed

import torch
from torch import nn
from torch import Tensor
from torch.nn import functional as F
from torch.nn.modules.loss import _Loss

class GaussianNLLLoss(_Loss):
    def __init__(self, full: bool = False, eps: float = 1e-6, reduction: str = 'mean', require_grad_var=False, require_grad_mean=True) -> None:
        if require_grad_var == require_grad_mean:
            raise ValueError("Either require_grad_var or require_grad_mean must be true")

mohmdelsayed

from torch.nn.modules.loss import _Loss
import torch
from torch import Tensor
from torch.nn import functional as F

class MultiLabelNLLoss(_Loss):
      def __init__(self, reduction = 'mean'):
          self.reduction = reduction
          super(MultiLabelNLLoss, self).__init__(reduction=reduction)