Usage

EthicalSmartGrid provides a PettingZoo 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 PettingZoo API as close as possible, such that it can be used with any PettingZoo-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.

To create an environment, type in a Python console:

from smartgrid import make_basic_smartgrid
env = make_basic_smartgrid()

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

done = False
obs, _ = env.reset()
while not done:
    # Replace the `actions` dict with your own learning algorithm here
    actions = {
        agent_name: env.action_space(agent_name).sample()
        for agent_name in env.agents
    }
    obs, reward_n, terminated_n, truncated_n, info_n = env.step(actions)
    # Print the rewards received by the learning agents during this step
    print(reward_n)
    done = all(terminated_n) or all(truncated_n)

The rewards received by the learning agents (reward_n) each step can be useful to analyze and visualize the quality of the learned behaviours. In this example, we simply print them, which has the additional advantage of showing that “something happens” at each time step; yet, most users will probably want to collect them and to display them in a plot. Please refer to Visualizing results to see an example of how to do so.

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. You can also see a few pre-defined scenarii to quickly launch experiments, without having to code the setup yourself; you may also take inspiration from these scenarii for custom setups.

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