{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n",
      "[1 0]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "y_out = [[[-0.52742714, -0.8918941 , -0.53989583, -0.874211 ]]]\n",
    "\n",
    "outputs = y_out[0][0]\n",
    "\n",
    "prob_action1 = outputs[:2]\n",
    "prob_action2 = outputs[2:]\n",
    "\n",
    "norm_action1 = [float(i)/sum(prob_action1) for i in prob_action1]\n",
    "norm_action2 = [float(i)/sum(prob_action2) for i in prob_action2]\n",
    "\n",
    "action1 = np.random.multinomial(1,norm_action1)\n",
    "action2 = np.random.multinomial(1,norm_action2)\n",
    "print(action1)\n",
    "print(action2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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