Customize_env

Customize Environment Tutorial

This tutorial provides a step-by-step guide for customizing the environment in the RL-ADN framework. Customizing the environment allows users to tailor the simulation to specific research needs, experiment with different configurations, and evaluate the performance of DRL algorithms under various conditions.

Overview

The Customize_env.ipynb notebook covers the following steps:

1. Setting up the environment configuration
2. Defining custom components
3. Integrating custom components into the environment
4. Running the customized environment

Step-by-Step Guide

1. Setting Up the Environment Configuration

First, import the necessary libraries and set up the initial environment configuration. Ensure that the RL-ADN package is installed.

import gym
import numpy as np
from RL_ADN import RL_ADN_Environment

# Define the configuration for the custom environment
custom_config = {
    'network_data': 'path_to_network_data',
    'time_series_data': 'path_to_time_series_data',
    'ess_model': 'path_to_ess_model',
    'other_parameters': 'value'
}

# Initialize the custom RL-ADN environment
env = RL_ADN_Environment(config=custom_config)