t-trainingdata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "#!pip install gym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import random\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the Cart Pole OpenAI Gym game environment and define some constants"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make('CartPole-v1')\n",
    "env.reset()\n",
    "goal_steps = 500\n",
    "score_requirement = 60\n",
    "intial_games = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please implement a funtion *model_data_preparation* which runs *intial_games* with a maximum of *goal_steps* steps. Please sum up the *reward* for each step as *score*. If *score* >= *score_requirement*, please record all observations and actions.\n",
    "\n",
    "IMPORTANT\n",
    "Please record the previous obervation for the current action since it was the previous action where the current action lead to a reward.\n",
    "\n",
    "The return type is *list*\n",
    "\n",
    "The format of the return list elements is:\n",
    "[array([ 0.01923121, -0.15798583, -0.03413126,  0.28463049]), [0, 1], ...]\n",
    "\n",
    "The type is list(list_of_length_2(nparray,list_of_length_2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_data_preparation():\n",
    "    training_data = []\n",
    "\n",
    "\n",
    "        \n",
    "        env.reset()\n",
    "\n",
    "    \n",
    "    return training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = model_data_preparation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(training_data[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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