{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "74b633f7",
   "metadata": {},
   "source": [
    "# Random Actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "281b5644",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from graph_jsp_env.disjunctive_graph_jsp_env import DisjunctiveGraphJspEnv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "100c9a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "jsp = np.array([\n",
    "    [[1, 2, 0],  # job 0\n",
    "     [0, 2, 1]],  # job 1\n",
    "    [[17, 12, 19],  # task durations of job 0\n",
    "     [8, 6, 2]]  # task durations of job 1\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e41b913",
   "metadata": {},
   "outputs": [],
   "source": [
    "env = DisjunctiveGraphJspEnv(\n",
    "    jps_instance=jsp,\n",
    "    perform_left_shift_if_possible=True,\n",
    "    normalize_observation_space=True,  # see documentation of DisjunctiveGraphJspEnv::get_state for more information\n",
    "    flat_observation_space=True,  # see documentation of DisjunctiveGraphJspEnv::get_state for more information\n",
    "    action_mode='task',  # alternative 'job'\n",
    "    dtype='float32'  # dtype of the observation space\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cf9d54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "terminated = False\n",
    "info = {}\n",
    "for i in range(6):\n",
    "    # get valid action mask. sample expects it to be a numpy array of type int8\n",
    "    mask = np.array(env.valid_action_mask()).astype(np.int8)\n",
    "    action = env.action_space.sample(mask=mask)\n",
    "    state, reward, terminated, truncated, info = env.step(action)\n",
    "    # chose the visualisation you want to see using the show parameter\n",
    "    # console rendering\n",
    "    env.render(show=[\"gantt_console\", \"graph_console\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f499f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"makespan: {info['makespan']}\")"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}