kenzotakahashi

function magnitude(vx, vy) {
  return Math.sqrt(vx**2 + vy**2)
}

function slope(x1, y1, x2, y2) {
  return (y2 - y1) / (x2 - x1)
}

function isParallel(l1, l2) {
  const [[x1,y1],[x2,y2]] = l1

kenzotakahashi

const { GraphQLServer } = require('graphql-yoga')
const mongoose = require('mongoose')
require('dotenv').config()
const resolvers = require('./resolvers')

mongoose.connect(process.env.MONGODB_URI)
const db = mongoose.connection
db.on("error", console.error.bind(console, "connection error"))
db.once("open", function(callback){
  console.log("Connection Succeeded")

kenzotakahashi

"""
REINFORCE(Policy Gradient)
"""

import collections
import gym
import numpy as np
import tensorflow as tf

from keras.models import Model

kenzotakahashi

import numpy as np
from sklearn.model_selection import train_test_split

import torch
import torch.nn as nn
from torch.autograd import Variable

np.random.seed(1337)

MAX_LEN = 30