DuelQLeapNet: D3QN with LeapNet
TODO reference the original papers ESANN Paper Leap Net
That has now be implemented as a github repository Leap Net Github
Description
The Leap is a type of neural network that has showed really good performances on the predictions of flows on powerlines based on the injection and the topology.
In this baseline, we use this very same architecture to model the Q function. The D3QN RL method is used.
An example to train this model is available in the train function Example-leapnet.
Warning
This baseline recodes entire the RL training procedure. You can use it if you want to have a deeper look at Deep Q Learning algorithm and a possible (non optimized, slow, etc. implementation ).
For a much better implementation, you can reuse the code of l2rpn_baselines.PPO_RLLIB
or the l2rpn_baselines.PPO_SB3
baseline.
Exported class
You can use this class with:
from l2rpn_baselines.DuelQLeapNet import train, evaluate, DuelQLeapNet
Other non exported class
These classes need to be imported, if you want to import them with (non exhaustive list):
from l2rpn_baselines.DuelQLeapNet.duelQLeapNet_NN import DuelQLeapNet_NN
from l2rpn_baselines.DuelQLeapNet.leapNet_NNParam import LeapNet_NNParam