DeepQSimple: A simple implementation of the Deep Q Learning

Description

This file serves as an concrete example on how to implement a baseline, even more concretely than the “do nothing” baseline. Don’t expect to obtain state of the art method with this simple method however.

An example to train this model is available in the train function Examples

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.DeepQSimple import train, evaluate, DeepQSimple

Classes:

`DeepQSimple`(action_space, nn_archi[, name, ...])	A simple deep q learning algorithm.
`DeepQ_NNParam`(action_size, observation_size, ...)	This defined the specific parameters for the DeepQ network.

Functions:

`evaluate`(env[, name, load_path, logs_path, ...])	How to evaluate the performances of the trained `DeepQSimple` agent.
`train`(env[, name, iterations, save_path, ...])	This function implements the "training" part of the balines "DeepQSimple".

class l2rpn_baselines.DeepQSimple.DeepQSimple(action_space, nn_archi, name='DeepQAgent', store_action=True, istraining=False, filter_action_fun=None, verbose=False, observation_space=None, **kwargs_converters)[source]: A simple deep q learning algorithm. It does nothing different thant its base class.

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 “PPO_RLLIB” or the “PPO_SB3” baseline.

class l2rpn_baselines.DeepQSimple.DeepQ_NNParam(action_size, observation_size, sizes, activs, list_attr_obs)[source]

This defined the specific parameters for the DeepQ network.

Nothing really different compared to the base class except that l2rpn_baselines.utils.NNParam.nn_class (nn_class) is deepQ_NN.DeepQ_NN .. 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 "PPO_RLLIB"
or the "PPO_SB3" baseline.

Classes:

nn_class

alias of DeepQ_NN

nn_class

alias of DeepQ_NN Methods:

construct_q_network()

This function will make 2 identical models, one will serve as a target model, the other one will be trained regurlarly.

l2rpn_baselines.DeepQSimple.evaluate(env, name='DeepQSimple', load_path=None, logs_path='./logs-eval/do-nothing-baseline', nb_episode=1, nb_process=1, max_steps=-1, verbose=False, save_gif=False, filter_action_fun=None)[source]

How to evaluate the performances of the trained DeepQSimple agent.

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 “PPO_RLLIB” or the “PPO_SB3” baseline.

Parameters:

env (grid2op.Environment) – The environment on which you evaluate your agent.
name (str) – The name of the trained baseline
load_path (str) – Path where the agent has been stored
logs_path (str) – Where to write the results of the assessment
nb_episode (str) – How many episodes to run during the assessment of the performances
nb_process (int) – On how many process the assessment will be made. (setting this > 1 can lead to some speed ups but can be unstable on some plaform)
max_steps (int) – How many steps at maximum your agent will be assessed
verbose (bool) – Currently un used
save_gif (bool) – Whether or not you want to save, as a gif, the performance of your agent. It might cause memory issues (might take a lot of ram) and drastically increase computation time.

Returns:

agent (l2rpn_baselines.utils.DeepQAgent) – The loaded agent that has been evaluated thanks to the runner.
res (list) – The results of the Runner on which the agent was tested.

Examples

You can evaluate a DeepQSimple this way:

from grid2op.Reward import L2RPNSandBoxScore, L2RPNReward
from l2rpn_baselines.DeepQSimple import eval

# Create dataset env
env = make("l2rpn_case14_sandbox",
           reward_class=L2RPNSandBoxScore,
           other_rewards={
               "reward": L2RPNReward
           })

# Call evaluation interface
evaluate(env,
         name="MyAwesomeAgent",
         load_path="/WHERE/I/SAVED/THE/MODEL",
         logs_path=None,
         nb_episode=10,
         nb_process=1,
         max_steps=-1,
         verbose=False,
         save_gif=False)

l2rpn_baselines.DeepQSimple.train(env, name='DeepQSimple', iterations=1, save_path=None, load_path=None, logs_dir=None, training_param=None, filter_action_fun=None, kwargs_converters={}, kwargs_archi={}, verbose=True)[source]

This function implements the “training” part of the balines “DeepQSimple”.

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 “PPO_RLLIB” or the “PPO_SB3” baseline.

Parameters:

env (grid2op.Environment) – Then environment on which you need to train your agent.
name (str`) – The name of your agent.
iterations (int) – For how many iterations (steps) do you want to train your agent. NB these are not episode, these are steps.
save_path (str) – Where do you want to save your baseline.
load_path (str) – If you want to reload your baseline, specify the path where it is located. NB if a baseline is reloaded some of the argument provided to this function will not be used.
logs_dir (str) – Where to store the tensorboard generated logs during the training. None if you don’t want to log them.
training_param (l2rpn_baselines.utils.TrainingParam) – The parameters describing the way you will train your model.
filter_action_fun (function) – A function to filter the action space. See IdToAct.filter_action documentation.
verbose (bool) – If you want something to be printed on the terminal (a better logging strategy will be put at some point)
kwargs_converters (dict) – A dictionary containing the key-word arguments pass at this initialization of the grid2op.Converter.IdToAct that serves as “Base” for the Agent.
kwargs_archi (dict) – Key word arguments used for making the DeepQ_NNParam object that will be used to build the baseline.

Returns:

baseline – The trained baseline.

Return type:

DeepQSimple

Examples

Here is an example on how to train a DeepQSimple baseline.

First define a python script, for example

import grid2op
from grid2op.Reward import L2RPNReward
from l2rpn_baselines.utils import TrainingParam, NNParam
from l2rpn_baselines.DeepQSimple import train

# define the environment
env = grid2op.make("l2rpn_case14_sandbox",
                   reward_class=L2RPNReward)

# use the default training parameters
tp = TrainingParam()

# this will be the list of what part of the observation I want to keep
# more information on https://grid2op.readthedocs.io/en/latest/observation.html#main-observation-attributes
li_attr_obs_X = ["day_of_week", "hour_of_day", "minute_of_hour", "prod_p", "prod_v", "load_p", "load_q",
                 "actual_dispatch", "target_dispatch", "topo_vect", "time_before_cooldown_line",
                 "time_before_cooldown_sub", "rho", "timestep_overflow", "line_status"]

# neural network architecture
observation_size = NNParam.get_obs_size(env, li_attr_obs_X)
sizes = [800, 800, 800, 494, 494, 494]  # sizes of each hidden layers
kwargs_archi = {'observation_size': observation_size,
                'sizes': sizes,
                'activs': ["relu" for _ in sizes],  # all relu activation function
                "list_attr_obs": li_attr_obs_X}

# select some part of the action
# more information at https://grid2op.readthedocs.io/en/latest/converter.html#grid2op.Converter.IdToAct.init_converter
kwargs_converters = {"all_actions": None,
                     "set_line_status": False,
                     "change_bus_vect": True,
                     "set_topo_vect": False
                     }
# define the name of the model
nm_ = "AnneOnymous"
try:
    train(env,
          name=nm_,
          iterations=10000,
          save_path="/WHERE/I/SAVED/THE/MODEL",
          load_path=None,
          logs_dir="/WHERE/I/SAVED/THE/LOGS",
          training_param=tp,
          kwargs_converters=kwargs_converters,
          kwargs_archi=kwargs_archi)
finally:
    env.close()

Other non exported class

These classes need to be imported, if you want to import them with (non exhaustive list):

from l2rpn_baselines.DeepQSimple.DeepQ_NN import DeepQ_NN

class l2rpn_baselines.DeepQSimple.deepQ_NN.DeepQ_NN(nn_params, training_param=None)[source]

Constructs the desired deep q learning network

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 “PPO_RLLIB” or the “PPO_SB3” baseline.

schedule_lr_model: The schedule for the learning rate.

Methods:

construct_q_network()

This function will make 2 identical models, one will serve as a target model, the other one will be trained regurlarly.

construct_q_network()[source]: This function will make 2 identical models, one will serve as a target model, the other one will be trained regurlarly.