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All Projects → rte-france → l2rpn-baselines

rte-france / l2rpn-baselines

Licence: MPL-2.0, MPL-2.0 licenses found Licenses found MPL-2.0 LICENSE MPL-2.0 LICENSE.md
L2RPN Baselines a repository to host baselines for l2rpn competitions.

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reinforcement-learning-algorithmsbaselinepowergrid-operationgrid2op

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L2RPN_Baselines

Repository hosting reference baselines for the L2RPN challenge

Install

Requirements

python3 >= 3.6

Instal from PyPI

pip3 install l2rpn_baselines

Install from source

git clone https://github.com/rte-france/l2rpn-baselines.git
cd l2rpn-baselines
pip3 install -U .
cd ..
rm -rf l2rpn-baselines

Contribute

We welcome contributions: see the contribute guide for details.

Get started with a baseline

Say you want to know how you compared with the "DoubleDuelingDQN" baseline implementation in this repository (for the sake of this example).

Train it (optional)

As no weights are provided for this baselines by default (yet), you will first need to train such a baseline:

import grid2op
from l2rpn_baselines.DoubleDuelingDQN import train
env = grid2op.make()
res = train(env, save_path="THE/PATH/TO/SAVE/IT", iterations=100)

You can have more information about extra argument of the "train" function in the CONTRIBUTE file.

Evaluate it

Once trained, you can reload it and evaluate its performance with the provided "evaluate" function:

import grid2op
from l2rpn_baselines.DoubleDuelingDQN import evaluate
env = grid2op.make()
res = evaluate(env, load_path="THE/PATH/TO/LOAD/IT.h5", nb_episode=10)

You can have more information about extra argument of the "evaluate" function in the CONTRIBUTE file.

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