refactored

intelligent-environments-lab · Nov 12, 2023 · 3c48731 · 3c48731
1 parent 211822a
commit 3c48731
Showing 1 changed file with 6 additions and 49 deletions.
diff --git a/examples/citylearn_rlem23_tutorial.ipynb b/examples/citylearn_rlem23_tutorial.ipynb
@@ -1,45 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/intelligent-environments-lab/CityLearn/blob/master/examples/citylearn_rlem23_tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "dzeHYa5GCxN7"
-      },
-      "outputs": [],
-      "source": [
-        "# MIT License\n",
-        "#\n",
-        "#@title Copyright (c) 2023 CCAI Community Authors { display-mode: \"form\" }\n",
-        "#\n",
-        "# Permission is hereby granted, free of charge, to any person obtaining a\n",
-        "# copy of this software and associated documentation files (the \"Software\"),\n",
-        "# to deal in the Software without restriction, including without limitation\n",
-        "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
-        "# and/or sell copies of the Software, and to permit persons to whom the\n",
-        "# Software is furnished to do so, subject to the following conditions:\n",
-        "#\n",
-        "# The above copyright notice and this permission notice shall be included in\n",
-        "# all copies or substantial portions of the Software.\n",
-        "#\n",
-        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
-        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
-        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
-        "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
-        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
-        "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
-        "# DEALINGS IN THE SOFTWARE."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -419,7 +379,8 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "6SbrWM_mnEDd"
+        "id": "6SbrWM_mnEDd",
+        "collapsed": true
       },
       "outputs": [],
       "source": [
@@ -3762,15 +3723,11 @@
         "id": "Sc1_SxLlLoqU"
       },
       "source": [
-        "## The CityLearn Challenge 2022\n",
-        "\n",
-        "[The CityLearn Challenge 2022](https://www.aicrowd.com/challenges/neurips-2022-citylearn-challenge) focuses on the opportunity brought on by home battery storage devices and photovoltaics and utilizes the same dataset used in this tutorial.\n",
-        "\n",
-        "While `The CityLearn Challenge 2022` has ended, you can still make submissions to the [Post Challenge Leaderboard](https://www.aicrowd.com/challenges/neurips-2022-citylearn-challenge/leaderboards?challenge_round_id=1294) and compare your scores to the challenge participants.\n",
+        "## The CityLearn Challenge 2023\n",
         "\n",
-        "Follow the instructions in the [starter-kit](https://gitlab.aicrowd.com/aicrowd/challenges/citylearn-challenge-2022/citylearn-2022-starter-kit#neurips-2022-citylearn-challenge-starter-kit) to begin participating!\n",
+        "[The CityLearn Challenge 2023](https://www.aicrowd.com/challenges/neurips-2023-citylearn-challenge) addresses this multi-faceted nature of advanced control in buildings by blending the challenges of control algorithm design, forecast quality and grid-resilience. The CityLearn Challenge 2023 presents a control track as done in previous challenges as well as introduces an independent forecast track where, both tracks are run in parallel and utilize the same dataset.\n",
         "\n",
-        "Also, be on the lookout for [the latest The CityLearn Challenge](https://www.citylearn.net/citylearn_challenge/index.html)!"
+        "In the control track, participants will develop energy management agent(s) and an optional custom reward function (in RLC solutions) to manage electrical and domestic hot water energy storage systems, and heat pump power in a synthetic single-family neighborhood under normal grid-operation and power outages. Whereas, in the forecast problem, participants will design regression models to predict the 48-hour-ahead end-use load profiles for each building in the neighborhood as well as the neighborhood-level 48-hour-ahead solar generation and carbon intensity profiles."
       ]
     },
     {
@@ -3826,4 +3783,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}