{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Actions as vector, and RL agent training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is recommended to have a look at the [0_basic_functionalities](0_basic_functionalities.ipynb), [1_Observation_Agents](1_Observation_Agents.ipynb) and [2_Action_GridManipulation](2_Action_GridManipulation.ipynb) notebooks before getting into this one." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Objectives**\n", "\n", "In this notebook we will expose :\n", "* how to use the \"converters\": some specific action_space that allows to manipulate a specific action representation\n", "* how to train a (stupid) Agent using reinforcement learning.\n", "* how to inspect (rapidly) the action taken by the Agent\n", "\n", "**NB** for this tutorial we train an Agent inspired from this blog post: [deep-reinforcement-learning-tutorial-with-open-ai-gym](https://towardsdatascience.com/deep-reinforcement-learning-tutorial-with-open-ai-gym-c0de4471f368). Many other different reinforcement learning tutorial exist. The code showed in this notebook has no pretention except to demonstrate how to use Grid2Op functionality to train a Deep Reinforcement learning Agent and inspect its behaviour. There are absolutely nothing implied about the performance, training strategy, type of Agent, meta parameters etc. All of them are purely \"random\".\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import grid2op" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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