Usage

EthicalSmartGrid provides a Gymnasium environment for multi-agent reinforcement learning of ethically-aligned behaviours, i.e., behaviours that take into account ethical considerations.

The environment is available as a Python package, and follows the Gymnasium API as close as possible, such that it can be used with any Gymnasium-compliant learning algorithm with little to no modification.

Installation

To use this simulator, you may either:

  • clone the repository: git clone https://github.com/ethicsai/ethical-smart-grid

  • or install the package from PyPi: pip install ethical-smart-grid

Cloning is the recommended way to get the up-to-date version, and is easier if you intend to implement new algorithms and/or extend the simulator. Cloning also allows you to refer to data files by relative paths, whereas downloading the package from PyPi requires to refer to data files as package resources.

Running a simulation

The environment is designed to allow various scenarii and to be highly configurable and extensible. To simplify the creation of a basic environment, the method smartgrid.make_basic_smartgrid() is made available. This method is also used to register the Smart Grid environment with Gymnasium, using the gym.make() method.

To create an environment, type in a Python console:

from smartgrid import make_basic_smartgrid
env = make_basic_smartgrid()

Or, equivalently:

import gymnasium as gym
import smartgrid

env = gym.make('EthicalSmartGrid-v0')

Then, the environment can be used through the standard interaction loop:

done = False
obs = env.reset()
while not done:
    # Replace the `actions` array with your own learning algorithm here
    actions = [
        agent.profile.action_space.sample()
        for agent in env.agents
    ]
    obs, reward_n, terminated_n, truncated_n, info_n = env.step(actions)
    done = all(terminated_n) or all(truncated_n)

In order to fully customize the environment setup, e.g., to control the number and profiles of agents, the available energy in the world, the reward function, etc., please refer to the Custom scenario documentation page.

The environment can also be extended to add your own components or replace existing ones; please refer to Why extending to do so.