HenryJia

# Based on https://scipy-lectures.org/packages/scikit-learn/auto_examples/plot_tsne.html
# tSNE: https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding
# MNIST Dataset: http://yann.lecun.com/exdb/mnist/

import struct

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

HenryJia

import time, os, sys, argparse
import numpy as np # linear algebra 
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
np.random.seed(94103)

import torch
import torch.nn as nn
from torch.optim import Adam
from torch.utils.data import DataLoader

HenryJia

import time
import numpy as np
import torch
from lighter.train import AsynchronousLoader
from torch.utils.data import DataLoader

batch_size = 2 ** 13
length = 100
device = torch.device('cuda:0')

HenryJia

import numpy as np
import gym

def sigmoid(x):
    return 1.0 / (1.0 + np.exp(-x))

env = gym.make('CartPole-v1')
desired_state = np.array([0, 0, 0, 0])
desired_mask = np.array([0, 0, 1, 0])