DoubleDuelingDQN: A example implementation of Double Duelling Deep Q Network


This module serves as an concrete example on how to implement a D3QN baseline. This baseline is of type Double Duelling Deep Q Network, as in Duelling Q Network and DoubleQ update.

It’s main purpose is to provide an example of this network type running with Grid2Op. However, don’t expect to obtain state of the art results.

Agent class

You can use this class with:

from l2rpn_baselines.DoubleDuelingDQN import DoubleDuelingDQN
from l2rpn_baselines.DoubleDuelingDQN import train
from l2rpn_baselines.DoubleDuelingDQN import evaluate


Training a model requires tweaking many hyperparameters, these can be found in a specific class attributes:

from l2rpn_baselines.DoubleDuelingDQN import DoubleDuelingDQNConfig

# Set hyperparameters before training
DoubleDuelingDQNConfig.LR = 1e-5
DoubleDuelingDQNConfig.INITAL_EPSILON = 1.0
DoubleDuelingDQNConfig.FINAL_EPSILON = 0.001
DoubleDuelingDQNConfig.DECAY_EPSILON = 10000

Internal classes

The neural network model is defined in a separate class. You may want to import it manually:

from l2rpn_baselines.DoubleDuelingDQN.DoubleDuelingDQN_NN import DoubleDuelingDQN_NN