It is recommended to have a look at the 0_basic_functionalities notebook before getting into this one.
Objective
This notebook will cover the basics of how to "code" an Agent that takes actions on the powergrid. Examples of "expert agents" that can take actions based on some fixed rules, will be given. More generic types of Agents, relying for example on machine learning / deep learning will be covered in the notebook 3_TrainingAnAgent.
This notebook will also cover the description of the Observation class, which is useful to take some actions.
import os
import sys
import grid2op
res = None
try:
from jyquickhelper import add_notebook_menu
res = add_notebook_menu()
except ModuleNotFoundError:
print("Impossible to automatically add a menu / table of content to this notebook.\nYou can download \"jyquickhelper\" package with: \n\"pip install jyquickhelper\"")
res
Impossible to automatically add a menu / table of content to this notebook. You can download "jyquickhelper" package with: "pip install jyquickhelper"
In this paragraph we will cover the observation class. For more information about it, we recommend to have a look at the official documentation, or here or in the Observations.py files for more information. Only basic concepts are detailed in this notebook.
An observation can be accessed by calling env.step()
. The next cell is dedicated to creating an environment and getting an observation instance. We use the default rte_case14_realistic
environment from Grid2Op framework.
env = grid2op.make(test=True)
/home/benjamin/Documents/grid2op_dev/getting_started/grid2op/MakeEnv/Make.py:240: UserWarning: You are using a development environment. This environment is not intended for training agents. warnings.warn(_MAKE_DEV_ENV_WARN)
To perform a step, as stated on the short description above, we need an action. More information about actions is given in the 2_ActionRepresentation notebook. Here we use a DoNothingAgent, that does nothing. obs is the observation of the environment.
do_nothing_act = env.helper_action_player({})
obs, reward, done, info = env.step(do_nothing_act)
In this notebook we will detail only the "CompleteObservation". Grid2Op
allows to model different kinds of observations. For example, some observations could have incomplete data, or noisy data, etc. CompleteObservation
gives the full state of the powergrid, without any noise. It's the default type of observation used.
An observation has calendar data (eg the time stamp of the observation):
obs.year, obs.month, obs.day, obs.hour_of_day, obs.minute_of_hour, obs.day_of_week
(2019, 1, 6, 0, 5, 6)
It has some powergrid generic information:
print("Number of generators of the powergrid: {}".format(obs.n_gen))
print("Number of loads of the powergrid: {}".format(obs.n_load))
print("Number of powerline of the powergrid: {}".format(obs.n_line))
print("Number of elements connected to each substations in the powergrid: {}".format(obs.sub_info))
print("Total number of elements: {}".format(obs.dim_topo))
Number of generators of the powergrid: 5 Number of loads of the powergrid: 11 Number of powerline of the powergrid: 20 Number of elements connected to each substations in the powergrid: [3 6 4 6 5 6 3 2 5 3 3 3 4 3] Total number of elements: 56
It has some information about the generators (each generator can be viewed as a point in a 3-dimensional space)
print("Generators active production: {}".format(obs.prod_p))
print("Generators reactive production: {}".format(obs.prod_q))
print("Generators voltage setpoint : {}".format(obs.prod_v))
Generators active production: [81.6 81.1 12.9 0. 77.7201] Generators reactive production: [ 21.790668 70.214264 48.05804 24.508774 -16.541656] Generators voltage setpoint : [142.1 142.1 22. 13.200001 142.1 ]
It has some information about the loads (each load is a point in a 3-dimensional space, too)
print("Loads active consumption: {}".format(obs.load_p))
print("Loads reactive consumption: {}".format(obs.prod_q))
print("Loads voltage (voltage magnitude of the bus to which it is connected) : {}".format(obs.load_v))
Loads active consumption: [25.4 84.8 45. 6.8 12.7 28.8 9.5 3.4 5.6 11.9 15.4] Loads reactive consumption: [ 21.790668 70.214264 48.05804 24.508774 -16.541656] Loads voltage (voltage magnitude of the bus to which it is connected) : [142.1 142.1 138.70158 139.39479 22. 21.092138 21.08566 21.453533 21.569204 21.430758 20.69996 ]
In this setting, a powerline can be viewed as a point in an 8-dimensional space:
for both its origin and its extremity.
For example, suppose the powerline line1
is connecting two node A
and B
. There are two separate values for the active flow on line1
: the active flow from A
to B
(origin) and the active flow from B
to A
(extremity).
These powerline features can be accessed with :
print("Origin active flow: {}".format(obs.p_or))
print("Origin reactive flow: {}".format(obs.q_or))
print("Origin current flow: {}".format(obs.a_or))
print("Origin voltage (voltage magnitude to the bus to which the origin end is connected): {}".format(obs.v_or))
print("Extremity active flow: {}".format(obs.p_ex))
print("Extremity reactive flow: {}".format(obs.q_ex))
print("Extremity current flow: {}".format(obs.a_ex))
print("Extremity voltage (voltage magnitude to the bus to which the origin end is connected): {}".format(obs.v_ex))
Origin active flow: [ 3.9922398e+01 3.7797703e+01 2.1780769e+01 4.0161697e+01 3.3859901e+01 1.7860813e+01 -2.8052214e+01 9.7739801e+00 7.7287230e+00 1.8051615e+01 3.3593693e+00 7.7858996e+00 -6.1440678e+00 2.0364611e+00 7.8760457e+00 2.5437183e+01 1.4508085e+01 3.5354317e+01 1.5543122e-14 -2.5437183e+01] Origin reactive flow: [-15.334058 -1.2075976 -7.0024953 0.663526 -0.383117 7.329237 -2.9436882 10.462834 5.576318 14.927625 -0.85521334 4.0310593 -7.464343 1.4842943 7.410275 -15.625636 -2.7032974 -5.641245 -23.634314 -5.573329 ] Origin current flow: [173.75766 153.64987 92.956 163.19867 137.5811 78.4405 117.409485 375.74683 250.10808 614.7267 94.88822 239.99174 264.71527 67.45319 291.3341 124.26482 61.42983 148.28416 918.84076 712.8031 ] Origin voltage (voltage magnitude to the bus to which the origin end is connected): [142.1 142.1 142.1 142.1 142.1 142.1 138.70158 22. 22. 22. 21.092138 21.092138 21.08566 21.569204 21.430758 138.70158 138.70158 139.39479 14.850535 21.092138] Extremity active flow: [-3.9602367e+01 -3.7068695e+01 -2.1560818e+01 -3.9274380e+01 -3.3242977e+01 -1.7618677e+01 2.8157352e+01 -9.6130629e+00 -7.6364613e+00 -1.7751646e+01 -3.3559325e+00 -7.6980476e+00 6.2130628e+00 -2.0243990e+00 -7.7019520e+00 -2.5437183e+01 -1.4508085e+01 -3.5354317e+01 -1.5987212e-14 2.5437183e+01] Extremity reactive flow: [ 10.712756 -0.90132874 3.285029 -1.4910296 -1.3327575 -8.036348 3.2753313 -10.125853 -5.3842945 -14.336893 0.86434317 -3.8441856 7.625853 -1.473381 -7.0558143 17.390247 3.8292 8.391265 24.508774 6.244066 ] Extremity current flow: [ 166.68697 153.57782 88.61224 163.59877 137.79755 80.60723 117.409485 375.74683 250.10808 614.7267 94.88822 239.99174 264.71527 67.45319 291.3341 1197.9484 410.72595 953.58575 1071.9808 1018.29016 ] Extremity voltage (voltage magnitude to the bus to which the origin end is connected): [142.1 139.39479 142.1 138.70158 139.39479 138.70158 139.39479 21.453533 21.569204 21.430758 21.08566 20.69996 21.453533 21.430758 20.69996 14.850535 21.092138 22. 13.200001 14.850535]
Another powerline feature is the $\rho$ ratio, ie. for each powerline, the ratio between the current flow in the powerline and its thermal limit. It can be accessed with:
obs.rho
array([0.45143566, 0.3991941 , 0.24462105, 0.42947018, 0.87631273, 0.20642236, 0.30897233, 0.34864962, 0.54149985, 0.7985534 , 0.3521812 , 0.62351686, 0.34830958, 0.1775084 , 0.38333434, 0.32284945, 0.26599896, 0.86817706, 0.27006915, 0.20950978], dtype=float32)
The observation (obs) also stores information on the topology and the state of the powerline.
obs.timestep_overflow # the number of timestep each of the powerline is in overflow (1 powerline per component)
obs.line_status # the status of each powerline: True connected, False disconnected
obs.topo_vect # the topology vector the each element (generator, load, each end of a powerline) to which the object
# is connected: 1 = bus 1, 2 = bus 2.
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)
In grid2op
, all objects (end of a powerline, load or generator) can be either disconnected, connected to the first bus of its substation, or connected to the second bus of its substation.
topo_vect
is the vector containing the connection information, it is part of the observation.
If an object is disconnected, then its corresponding component in topo_vect
will be -1
. If it's connected to the first bus of its substation, its component will be 1
and if it's connected to the second bus, its component will be 2
.
More information about this topology vector is given in the documentation here.
More information about this topology vector will be given in the notebook dedicated to vizualisation.
The observation can be converted to / from a flat numpy array. This function is useful for interacting with machine learning libraries or to store it, but it probably makes it less readable for a human. The function proceeds by stacking all the features mentionned above in a single numpy.float64
vector.
vector_representation_of_observation = obs.to_vect()
vector_representation_of_observation
array([ 2.01900000e+03, 1.00000000e+00, 6.00000000e+00, 0.00000000e+00, 5.00000000e+00, 6.00000000e+00, 8.15999985e+01, 8.10999985e+01, 1.28999996e+01, 0.00000000e+00, 7.77201004e+01, 2.17906685e+01, 7.02142639e+01, 4.80580406e+01, 2.45087738e+01, -1.65416565e+01, 1.42100006e+02, 1.42100006e+02, 2.20000000e+01, 1.32000008e+01, 1.42100006e+02, 2.53999996e+01, 8.48000031e+01, 4.50000000e+01, 6.80000019e+00, 1.26999998e+01, 2.87999992e+01, 9.50000000e+00, 3.40000010e+00, 5.59999990e+00, 1.18999996e+01, 1.53999996e+01, 1.77999992e+01, 5.95999985e+01, 3.07999992e+01, 4.59999990e+00, 8.69999981e+00, 1.97000008e+01, 6.59999990e+00, 2.50000000e+00, 3.90000010e+00, 8.39999962e+00, 1.08999996e+01, 1.42100006e+02, 1.42100006e+02, 1.38701584e+02, 1.39394791e+02, 2.20000000e+01, 2.10921383e+01, 2.10856609e+01, 2.14535332e+01, 2.15692043e+01, 2.14307575e+01, 2.06999607e+01, 3.99223976e+01, 3.77977028e+01, 2.17807693e+01, 4.01616974e+01, 3.38599014e+01, 1.78608131e+01, -2.80522137e+01, 9.77398014e+00, 7.72872305e+00, 1.80516148e+01, 3.35936928e+00, 7.78589964e+00, -6.14406776e+00, 2.03646111e+00, 7.87604570e+00, 2.54371834e+01, 1.45080853e+01, 3.53543167e+01, 1.55431223e-14, -2.54371834e+01, -1.53340578e+01, -1.20759761e+00, -7.00249529e+00, 6.63525999e-01, -3.83116990e-01, 7.32923698e+00, -2.94368815e+00, 1.04628344e+01, 5.57631779e+00, 1.49276247e+01, -8.55213344e-01, 4.03105927e+00, -7.46434307e+00, 1.48429430e+00, 7.41027498e+00, -1.56256361e+01, -2.70329738e+00, -5.64124489e+00, -2.36343136e+01, -5.57332897e+00, 1.42100006e+02, 1.42100006e+02, 1.42100006e+02, 1.42100006e+02, 1.42100006e+02, 1.42100006e+02, 1.38701584e+02, 2.20000000e+01, 2.20000000e+01, 2.20000000e+01, 2.10921383e+01, 2.10921383e+01, 2.10856609e+01, 2.15692043e+01, 2.14307575e+01, 1.38701584e+02, 1.38701584e+02, 1.39394791e+02, 1.48505354e+01, 2.10921383e+01, 1.73757660e+02, 1.53649872e+02, 9.29560013e+01, 1.63198669e+02, 1.37581100e+02, 7.84404984e+01, 1.17409485e+02, 3.75746826e+02, 2.50108078e+02, 6.14726685e+02, 9.48882217e+01, 2.39991745e+02, 2.64715271e+02, 6.74531937e+01, 2.91334106e+02, 1.24264816e+02, 6.14298286e+01, 1.48284164e+02, 9.18840759e+02, 7.12803101e+02, -3.96023674e+01, -3.70686951e+01, -2.15608177e+01, -3.92743797e+01, -3.32429771e+01, -1.76186771e+01, 2.81573524e+01, -9.61306286e+00, -7.63646126e+00, -1.77516460e+01, -3.35593247e+00, -7.69804764e+00, 6.21306276e+00, -2.02439904e+00, -7.70195198e+00, -2.54371834e+01, -1.45080853e+01, -3.53543167e+01, -1.59872116e-14, 2.54371834e+01, 1.07127562e+01, -9.01328743e-01, 3.28502893e+00, -1.49102962e+00, -1.33275747e+00, -8.03634834e+00, 3.27533126e+00, -1.01258526e+01, -5.38429451e+00, -1.43368931e+01, 8.64343166e-01, -3.84418559e+00, 7.62585306e+00, -1.47338104e+00, -7.05581427e+00, 1.73902473e+01, 3.82920003e+00, 8.39126492e+00, 2.45087738e+01, 6.24406624e+00, 1.42100006e+02, 1.39394791e+02, 1.42100006e+02, 1.38701584e+02, 1.39394791e+02, 1.38701584e+02, 1.39394791e+02, 2.14535332e+01, 2.15692043e+01, 2.14307575e+01, 2.10856609e+01, 2.06999607e+01, 2.14535332e+01, 2.14307575e+01, 2.06999607e+01, 1.48505354e+01, 2.10921383e+01, 2.20000000e+01, 1.32000008e+01, 1.48505354e+01, 1.66686966e+02, 1.53577820e+02, 8.86122437e+01, 1.63598770e+02, 1.37797546e+02, 8.06072311e+01, 1.17409485e+02, 3.75746826e+02, 2.50108078e+02, 6.14726685e+02, 9.48882217e+01, 2.39991745e+02, 2.64715271e+02, 6.74531937e+01, 2.91334106e+02, 1.19794836e+03, 4.10725952e+02, 9.53585754e+02, 1.07198083e+03, 1.01829016e+03, 4.51435655e-01, 3.99194092e-01, 2.44621053e-01, 4.29470181e-01, 8.76312733e-01, 2.06422359e-01, 3.08972329e-01, 3.48649621e-01, 5.41499853e-01, 7.98553407e-01, 3.52181196e-01, 6.23516858e-01, 3.48309577e-01, 1.77508399e-01, 3.83334339e-01, 3.22849452e-01, 2.65998960e-01, 8.68177056e-01, 2.70069152e-01, 2.09509775e-01, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], dtype=float32)
An observation can be copied, of course:
obs2 = obs.copy()
Or reset:
obs2.reset()
print(obs2.prod_p)
[nan nan nan nan nan]
Or loaded from a vector:
obs2.from_vect(vector_representation_of_observation)
obs2.prod_p
array([81.6 , 81.1 , 12.9 , 0. , 77.7201], dtype=float32)
It is also possible to assess whether two observations are equal or not:
obs == obs2
True
For this type of observation, it is also possible to retrieve the topology as a matrix. The topology matrix can be obtained in two different formats.
Format 1: the connectivity matrix
which has as many rows / columns as the number of elements in the powergrid (remember that an element is either an end of a powerline, or a generator or a load) and that tells if 2 elements are connected to one another or not:
obs.connectivity_matrix()
array([[0., 1., 1., ..., 0., 0., 0.], [1., 0., 1., ..., 0., 0., 0.], [1., 1., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 1., 1.], [0., 0., 0., ..., 1., 0., 1.], [0., 0., 0., ..., 1., 1., 0.]], dtype=float32)
This representation has the advantage to always have the same dimension, regardless of the topology of the powergrid.
Format 2: the bus connectivity matrix
has as many rows / columns as the number of active buses of the powergrid. It should be understood as follows:
obs.bus_connectivity_matrix()
array([[1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 1., 1., 1., 1., 0., 1., 0., 1., 0., 0., 0., 0., 0.], [1., 1., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1., 1., 0.], [0., 0., 0., 1., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 1., 0., 0., 1., 0., 1., 1., 0., 0., 0., 1.], [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0.], [0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 1., 0., 0., 0.], [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 1., 0.], [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 1., 1.], [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 1.]], dtype=float32)
As opposed to most RL problems, in this framework we add the possibility to "simulate" the impact of a possible action on the power grid. This helps calculating roll-outs in the RL setting, and can be close to "model-based" reinforcement learning approaches (except that nothing more has to be learned).
This "simulate" method uses the available forecast data (forecasts are made available by the same way we loaded the data here, with the class GridStateFromFileWithForecasts
. For this class, only forecasts for 1 time step are provided, but this might be adapted in the future).
Note that this simulate
function can use a different simulator than the one used by the Environment. Fore more information, we encourage you to read the official documentation, or if it has been built locally (recommended), to consult this page.
This function will:
simulate
powerflow simulatorFrom a user point of view, this is the main difference with the previous pypownet framework. In pypownet, this "simulation" used to be performed directly by the environment, thus giving direct access of the environment's future data to the agent, which could break the RL framework since the agent is only supposed to know about the current state of the environment (it was not the case in the first edition of the Learning to Run A Power Network as the Environment was fully observable). In grid2op, the simulation is now performed from the current state of the environment and it is imperfect since it does not have access to future information.
Here is an example of some features of the observation, in the current state and in the simulated next state :
do_nothing_act = env.helper_action_player({})
obs_sim, reward_sim, is_done_sim, info_sim = obs.simulate(do_nothing_act)
obs.prod_p
array([81.6 , 81.1 , 12.9 , 0. , 77.7201], dtype=float32)
obs_sim.prod_p
array([81.5 , 79.7 , 12.9 , 0. , 79.5781], dtype=float32)
In this section we will make our first Agent that will act based on these observations.
All Agents must derive from the grid2op.Agent class. The main function to implement for the Agents is the "act" function (more information can be found on the official documentation or here ).
Basically, the Agent receives a reward and an observation, and chooses a new action. Some different Agents are pre-defined in the grid2op
package. We won't talk about them here (for more information, see the documentation or the Agent.py file), but rather we will make a custom Agent.
This Agent will select among:
by using simulate
on the corresponding actions, and choosing the one that has the highest predicted reward.
Note that this kind of Agent is not particularly smart and is given only as an example.
More information about the creation / manipulation of Action will be given in the notebook 2_Action_GridManipulation
from grid2op.Agent import BaseAgent
import numpy as np
import pdb
class MyAgent(BaseAgent):
def __init__(self, action_space):
# python required method to code
BaseAgent.__init__(self, action_space)
self.do_nothing = self.action_space({})
self.print_next = False
def act(self, observation, reward, done=False):
i_max = np.argmax(observation.rho)
new_status_max = np.zeros(observation.rho.shape)
new_status_max[i_max] = -1
act_max = self.action_space({"set_line_status": new_status_max})
i_min = np.argmin(observation.rho)
new_status_min = np.zeros(observation.rho.shape)
if observation.rho[i_min] > 0:
# all powerlines are connected, i try to disconnect this one
new_status_min[i_min] = -1
act_min = self.action_space({"set_line_status": new_status_min})
else:
# at least one powerline is disconnected, i try to reconnect it
new_status_min[i_min] = 1
# act_min = self.action_space({"set_status": new_status_min})
act_min = self.action_space({"set_line_status": new_status_min,
"set_bus": {"lines_or_id": [(i_min, 1)], "lines_ex_id": [(i_min, 1)]}})
_, reward_sim_dn, *_ = observation.simulate(self.do_nothing)
_, reward_sim_max, *_ = observation.simulate(act_max)
_, reward_sim_min, *_ = observation.simulate(act_min)
if reward_sim_dn >= reward_sim_max and reward_sim_dn >= reward_sim_min:
self.print_next = False
res = self.do_nothing
elif reward_sim_max >= reward_sim_min:
self.print_next = True
res = act_max
print(res)
else:
self.print_next = True
res = act_min
print(res)
return res
We compare this Agent with the Donothing
agent (already coded) on the 3 episodes made available with this package. To make this comparison more interesting, it's better to use the L2RPN rewards (L2RPNReward
).
from grid2op.Runner import Runner
from grid2op.Agent import DoNothingAgent
from grid2op.Reward import L2RPNReward
from grid2op.Chronics import GridStateFromFileWithForecasts
max_iter = 10 # to make computation much faster we will only consider 50 time steps instead of 287
runner = Runner(**env.get_params_for_runner(),
agentClass=DoNothingAgent
)
res = runner.run(nb_episode=1, max_iter=max_iter)
print("The results for DoNothing agent are:")
for _, chron_name, cum_reward, nb_time_step, max_ts in res:
msg_tmp = "\tFor chronics with id {}\n".format(chron_name)
msg_tmp += "\t\t - cumulative reward: {:.6f}\n".format(cum_reward)
msg_tmp += "\t\t - number of time steps completed: {:.0f} / {:.0f}".format(nb_time_step, max_ts)
print(msg_tmp)
The results for DoNothing agent are: For chronics with id 000 - cumulative reward: 11076.768555 - number of time steps completed: 10 / 10
runner = Runner(**env.get_params_for_runner(),
agentClass=MyAgent
)
res = runner.run(nb_episode=1, max_iter=max_iter)
print("The results for the custom agent are:")
for _, chron_name, cum_reward, nb_time_step, max_ts in res:
msg_tmp = "\tFor chronics with id {}\n".format(chron_name)
msg_tmp += "\t\t - cumulative reward: {:.6f}\n".format(cum_reward)
msg_tmp += "\t\t - number of time steps completed: {:.0f} / {:.0f}".format(nb_time_step, max_ts)
print(msg_tmp)
The results for the custom agent are: For chronics with id 000 - cumulative reward: 11076.768555 - number of time steps completed: 10 / 10
As we can see, both agents obtain the same score here, but there would be a difference if we didn't limit the episode length to 10 time steps.
NB Disabling the time limit for the episode can be done by setting max_iter=-1
in the previous cells. Here, setting max_iter=10
is only done so that this notebook can run faster, but increasing or disabling the time limit would allow us to spot differences in the agents' performances.
The same can be done for the PowerLineSwitch
agent :
from grid2op.Agent import PowerLineSwitch
runner = Runner(**env.get_params_for_runner(),
agentClass=PowerLineSwitch
)
res = runner.run(nb_episode=1, max_iter=max_iter)
print("The results for the PowerLineSwitch agent are:")
for _, chron_name, cum_reward, nb_time_step, max_ts in res:
msg_tmp = "\tFor chronics with ID {}\n".format(chron_name)
msg_tmp += "\t\t - cumulative reward: {:.6f}\n".format(cum_reward)
msg_tmp += "\t\t - number of time steps completed: {:.0f} / {:.0f}".format(nb_time_step, max_ts)
print(msg_tmp)
The results for the PowerLineSwitch agent are: For chronics with ID 000 - cumulative reward: 2164.011719 - number of time steps completed: 3 / 10