grid2op.gym_compat
The gymnasium framework in reinforcement learning is widely used. Starting from version 1.2.0 we improved the compatibility with this framework.
Starting with 1.9.1 we switch (as advised) from the legacy "gym" framework to the new "gymnasium" framework (gym is no longer maintained since v0.26.2, see https://www.gymlibrary.dev/). This change should not have any impact on older grid2op code except that you now need to use import gymnasium as gym instead of import gym in your base code.
Note
If you want to still use the "legacy" gym classes you can still do it with grid2op: Backward compatibility with openai gym is maintained.
Note
By default, if gymnasium is installed, all default classes from grid2op.gym_compat module will inherit from gymnasium. You can still retrieve the classes inheriting from gym (and not gymnasium).
More information on the section gymnasium_gym
Before grid2op 1.2.0 only some classes fully implemented the open AI gym interface:
- the
grid2op.Environment
(with methods such as env.reset, env.step etc.) - the
grid2op.Agent
(with the agent.act etc.) - the creation of pre defined environments (with grid2op.make)
Starting from 1.2.0 we implemented some automatic converters that are able to automatically map grid2op representation for the action space and the observation space into open AI gym "spaces". More precisely these are represented as gym.spaces.Dict.
As of grid2op 1.4.0 we tighten the gap between openAI gym and grid2op by introducing the dedicated module grid2op.gym_compat . Withing this module there are lots of functionalities to convert a grid2op environment into a gym environment (that inherit gym.Env instead of "simply" implementing the open ai gym interface).
A simple usage is:
import grid2op
from grid2op.gym_compat import GymEnv
env_name = "l2rpn_case14_sandbox" # or any other grid2op environment name
g2op_env = grid2op.make(env_name) # create the gri2op environment
gym_env = GymEnv(g2op_env) # create the gym environment
# check that this is a properly defined gym environment:
import gym
print(f"Is gym_env and open AI gym environment: {isinstance(gym_env, gym.Env)}")
# it shows "Is gym_env and open AI gym environment: True"
Note
To be as close as grid2op as possible, by default (using the methode discribed above) the action space will be encoded as a gymnasium.spaces.Dict with keys the attribute of a grid2op action. This might not be the best representation to perform RL with (some framework do not really like it...)
For more customization on that side, please refer to the section gym_compat_box_discrete
below
Warning
The gym package has some breaking API change since its version 0.26. We attempted, in grid2op, to maintain compatibility both with former versions and later ones. This makes this class behave differently depending on the version of gym you have installed !
The main changes involve the functions env.step and env.reset (core gym functions)
This page is organized as follow:
Table of Contents
By default, the action space and observation space are gym.spaces.Dict with the keys being the attribute to modify.
For example, an observation space will look like:
- "_shunt_p": Box(env.n_shunt,) [type: float, low: -inf, high: inf]
- "_shunt_q": Box(env.n_shunt,) [type: float, low: -inf, high: inf]
- "_shunt_v": Box(env.n_shunt,) [type: float, low: -inf, high: inf]
- "_shunt_bus": Box(env.n_shunt,) [type: int, low: -inf, high: inf]
- "a_ex": Box(env.n_line,) [type: float, low: 0, high: inf]
- "a_or": Box(env.n_line,) [type: float, low: 0, high: inf]
- "active_alert": MultiBinary(env.dim_alerts)
- "actual_dispatch": Box(env.n_gen,)
- "alert_duration": Box(env.dim_alerts,) [type: int, low: 0, high: inf]
- "attack_under_alert": Box(env.dim_alerts,) [type: int, low: -1, high: inf]
- "attention_budget": Box(1,) [type: float, low: 0, high: inf]
- "current_step": Box(1,) [type: int, low: -inf, high: inf]
- "curtailment": Box(env.n_gen,) [type: float, low: 0., high: 1.0]
- "curtailment_limit": Box(env.n_gen,) [type: float, low: 0., high: 1.0]
- "curtailment_limit_effective": Box(env.n_gen,) [type: float, low: 0., high: 1.0]
- "day": Discrete(32)
- "day_of_week": Discrete(8)
- "delta_time": Box(0.0, inf, (1,), float32)
- "duration_next_maintenance": Box(env.n_line,) [type: int, low: -1, high: inf]
- "gen_margin_down": Box(env.n_gen,) [type: float, low: 0, high: env.gen_max_ramp_down]
- "gen_margin_up": Box(env.n_gen,) [type: float, low: 0, high: env.gen_max_ramp_up]
- "gen_p": Box(env.n_gen,) [type: float, low: env.gen_pmin, high: env.gen_pmax * 1.2]
- "gen_p_before_curtail": Box(env.n_gen,) [type: float, low: env.gen_pmin, high: env.gen_pmax * 1.2]
- "gen_q": Box(env.n_gen,) [type: float, low: -inf, high: inf]
- "gen_theta": Box(env.n_gen,) [type: float, low: -180, high: 180]
- "gen_v": Box(env.n_gen,) [type: float, low: 0, high: inf]
- "hour_of_day": Discrete(24)
- "is_alarm_illegal": Discrete(2)
- "line_status": MultiBinary(env.n_line)
- "load_p": Box(env.n_load,) [type: float, low: -inf, high: inf]
- "load_q": Box(env.n_load,) [type: float, low: -inf, high: inf]
- "load_theta": Box(env.n_load,) [type: float, low: -180, high: 180]
- "load_v": Box(env.n_load,) [type: float, low: -inf, high: inf]
- "max_step": Box(1,) [type: int, low: -inf, high: inf]
- "minute_of_hour": Discrete(60)
- "month": Discrete(13)
- "p_ex": Box(env.n_line,) [type: float, low: -inf, high: inf]
- "p_or": Box(env.n_line,) [type: float, low: -inf, high: inf]
- "q_ex": Box(env.n_line,) [type: float, low: -inf, high: inf]
- "q_or": Box(env.n_line,) [type: float, low: -inf, high: inf]
- "rho": Box(env.n_line,) [type: float, low: 0., high: inf]
- "storage_charge": Box(env.n_storage,) [type: float, low: 0., high: env.storage_Emax]
- "storage_power": Box(env.n_storage,) [type: float, low: -env.storage_max_p_prod, high: env.storage_max_p_absorb]
- "storage_power_target": Box(env.n_storage,) [type: float, low: -env.storage_max_p_prod, high: env.storage_max_p_absorb]
- "storage_theta": Box(env.n_storage,) [type: float, low: -180., high: 180.]
- "target_dispatch": Box(env.n_gen,) [type: float, low: -inf, high: inf]
- "thermal_limit": Box(env.n_line,) [type: int, low: 0, high: inf]
- "theta_ex": Box(env.n_line,) [type: float, low: -180., high: 180.]
- "theta_or": Box(env.n_line,) [type: float, low: -180., high: 180.]
- "time_before_cooldown_line": Box(env.n_line,) [type: int, low: 0, high: depending on parameters]
- "time_before_cooldown_sub": Box(env.n_sub,) [type: int, low: 0, high: depending on parameters]
- "time_next_maintenance": Box(env.n_line,) [type: int, low: 0, high: inf]
- "time_since_last_alarm": Box(1,) [type: int, low: -1, high: inf]
- "time_since_last_alert": Box(env.dim_alerts,) [type: int, low: -1, high: inf]
- "time_since_last_attack": Box(env.dim_alerts,) [type: int, low: -1, high: inf]
- "timestep_overflow": Box(env.n_line,) [type: int, low: 0, high: inf]
- "topo_vect": Box(env.dim_topo,) [type: int, low: -1, high: 2]
- "total_number_of_alert": Box(1 if env.dim_alerts > 0 else 0,) [type: int, low: 0, high: inf]
- "v_ex": Box(env.n_line,) [type: float, low: 0, high: inf]
- "v_or": Box(env.n_line,) [type: flaot, low: 0, high: inf]
- "was_alarm_used_after_game_over": Discrete(2)
- "was_alert_used_after_attack": Box(env.dim_alerts,) [type: int, low: -1, high: 1]
- "year": Discrete(2100)
Each keys correspond to an attribute of the observation. In this example "line_status": MultiBinary(20) represents the attribute obs.line_status which is a boolean vector (for each powerline True encodes for "connected" and False for "disconnected") See the chapter observation_module
for more information about these attributes.
You can transform the observation space as you wish. There are some examples in the notebooks.
The default action space is also a type of gym Dict. As for the observation space above, it is a straight translation from the attribute of the action to the key of the dictionary. This gives:
- "change_bus": MultiBinary(env.dim_topo)
- "change_line_status": MultiBinary(env.n_line)
- "curtail": Box(env.n_gen) [type: float, low=0., high=1.0]
- "redispatch": Box(env.n_gen) [type: float, low=-env.gen_max_ramp_down, high=`env.gen_max_ramp_up`]
- "set_bus": Box(env.dim_topo) [type: int, low=-1, high=2]
- "set_line_status": Box(env.n_line) [type: int, low=-1, high=1]
- "storage_power": Box(env.n_storage) [type: float, low=-env.storage_max_p_prod, high=`env.storage_max_p_absorb`]
- "raise_alarm": MultiBinary(env.dim_alarms)
- "raise_alert": MultiBinary(env.dim_alerts)
For example you can create a "gym action" (for the default encoding) like:
import grid2op
from grid2op.gym_compat import GymEnv
import numpy as np
env_name = ...
env = grid2op.make(env_name)
gym_env = GymEnv(env)
seed = ...
obs, info = gym_env.reset(seed) # for new gym interface
# do nothing
gym_act = {}
obs, reward, done, truncated, info = gym_env.step(gym_act)
#change the bus of the element 6 and 7 of the "topo_vect"
gym_act = {}
gym_act["change_bus"] = np.zeros(env.dim_topo, dtype=np.int8) # gym encoding of a multi binary
gym_act["change_bus"][[6, 7]] = 1
obs, reward, done, truncated, info = gym_env.step(gym_act)
# redispatch generator 2 of 1.7MW
gym_act = {}
gym_act["redispatch"] = np.zeros(env.n_gen, dtype=np.float32) # gym encoding of a Box
gym_act["redispatch"][2] = 1.7
obs, reward, done, truncated, info = gym_env.step(gym_act)
# set the bus of element 8 and 9 to bus 2
gym_act = {}
gym_act["set_bus"] = np.zeros(env.dim_topo, dtype=int) # gym encoding of a Box
gym_act["set_bus"][[8, 9]] = 2
obs, reward, done, truncated, info = gym_env.step(gym_act)
# of course, you can set_bus, redispatch, change the storage units etc. in the same action.
This way of doing things is perfectly grounded. It works but it is quite verbose and not really "ML friendly". You can customize the way you "encode" your actions / observations relatively easily. Some examples are given in the following subsections.
We offer some convenience functions to customize these spaces.
If you want a full control on this spaces, you need to implement something like:
import grid2op
env_name = ...
env = grid2op.make(env_name)
from grid2op.gym_compat import GymEnv
# this of course will not work... Replace "AGymSpace" with a normal gym space, like Dict, Box, MultiDiscrete etc.
from gym.spaces import AGymSpace
gym_env = GymEnv(env)
class MyCustomObservationSpace(AGymSpace):
def __init__(self, whatever, you, want):
# do as you please here
pass
# don't forget to initialize the base class
AGymSpace.__init__(self, see, gym, doc, as, to, how, to, initialize, it)
# eg. Box.__init__(self, low=..., high=..., dtype=float)
def to_gym(self, observation):
# this is this very same function that you need to implement
# it should have this exact name, take only one observation (grid2op) as input
# and return a gym object that belong to your space "AGymSpace"
return SomethingThatBelongTo_AGymSpace
# eg. return np.concatenate((obs.gen_p * 0.1, np.sqrt(obs.load_p))
gym_env.observation_space = MyCustomObservationSpace(whatever, you, wanted)
And for the action space:
import grid2op
env_name = ...
env = grid2op.make(env_name)
from grid2op.gym_compat import GymEnv
# this of course will not work... Replace "AGymSpace" with a normal gym space, like Dict, Box, MultiDiscrete etc.
from gym.spaces import AGymSpace
gym_env = GymEnv(env)
class MyCustomActionSpace(AGymSpace):
def __init__(self, whatever, you, want):
# do as you please here
pass
# don't forget to initialize the base class
AGymSpace.__init__(self, see, gym, doc, as, to, how, to, initialize, it)
# eg. MultiDiscrete.__init__(self, nvec=...)
def from_gym(self, gym_action):
# this is this very same function that you need to implement
# it should have this exact name, take only one action (member of your gym space) as input
# and return a grid2op action
return TheGymAction_ConvertedTo_Grid2op_Action
# eg. return np.concatenate((obs.gen_p * 0.1, np.sqrt(obs.load_p))
gym_env.action_space = MyCustomActionSpace(whatever, you, wanted)
There are some pre defined transformation (for example transforming the action to Discrete or MultiDiscrete). Do not hesitate to have a look at the section gym_compat_box_discrete
.
However, if you don't want to fully customize everything, we encourage you to have a look at the "GymConverter" that we coded to ease this process.
They all more or less the same manner. We show here an example of a "converter" that will scale the data (removing the value in substract and divide input data by divide):
import grid2op
from grid2op.gym_compat import GymEnv
from grid2op.gym_compat import ScalerAttrConverter
env_name = "l2rpn_case14_sandbox" # or any other grid2op environment name
g2op_env = grid2op.make(env_name) # create the gri2op environment
gym_env = GymEnv(g2op_env) # create the gym environment
ob_space = gym_env.observation_space
ob_space = ob_space.reencode_space("actual_dispatch",
ScalerAttrConverter(substract=0.,
divide=env.gen_pmax,
init_space=ob_space["actual_dispatch"]
)
)
gym_env.observation_space = ob_space
You can also add a specific keys into this observation space, for example say you want to compute the log of the loads instead of giving the direct value to your agent. This can be done with:
import grid2op
from grid2op.gym_compat import GymEnv
from grid2op.gym_compat import ScalerAttrConverter
env_name = "l2rpn_case14_sandbox" # or any other grid2op environment name
g2op_env = grid2op.make(env_name) # create the gri2op environment
gym_env = GymEnv(g2op_env) # create the gym environment
ob_space = gym_env.observation_space
shape_ = (g2op_env.n_load, )
ob_space = ob_space.add_key("log_load",
lambda obs: np.log(obs.load_p),
Box(shape=shape_,
low=np.full(shape_, fill_value=-np.inf, dtype=float),
high=np.full(shape_, fill_value=-np.inf, dtype=float),
dtype=float
)
)
gym_env.observation_space = ob_space
# and now you will get the key "log_load" as part of your gym observation.
A detailed list of such "converter" is documented on the section "Detailed Documentation by class". In the table below we describe some of them (nb if you notice a converter is not displayed there, do not hesitate to write us a "feature request" for the documentation, thanks in advance)
Converter name | Objective |
---|---|
ContinuousToDiscreteConverter |
Convert a continuous space into a discrete one |
MultiToTupleConverter |
Convert a gym MultiBinary to a gym Tuple of gym Binary and a gym MultiDiscrete to a Tuple of Discrete |
ScalerAttrConverter |
Allows to scale (divide an attribute by something and subtract something from it) |
BaseGymSpaceConverter.add_key | Allows you to compute another "part" of the observation space (you add an information to the gym space) |
BaseGymSpaceConverter.keep_only_attr | Allows you to specify which part of the action / observation you want to keep |
BaseGymSpaceConverter.ignore_attr | Allows you to ignore some attributes of the action / observation (they will not be part of the gym space) |
Warning
TODO: Help more than welcome !
Organize this page with a section for each "use":
- scale de the data
- keep only some part of the observation
- add some info to the observation
- transform a box to a discrete action space
- use MultiDiscrete
Instead of having the current ordering of things
Note
With the "converters" above, note that the observation space AND action space will still inherit from gym Dict.
They are complex spaces that are not well handled by some RL framework.
These converters only change the keys of these dictionaries !
The use of the converter above is nice if you can work with gym Dict, but in some cases, or for some frameworks it is not convenient to do it at all.
TO alleviate this problem, we developed 4 types of gym action space, following the architecture detailed in subsection base_gym_space_function
Converter name | Objective |
---|---|
BoxGymObsSpace |
Convert the observation space to a single "Box" |
BoxGymActSpace |
Convert a gym MultiBinary to a gym Tuple of gym Binary and a gym MultiDiscrete to a Tuple of Discrete |
MultiDiscreteActSpace |
Allows to scale (divide an attribute by something and subtract something from it) |
DiscreteActSpace |
Allows you to compute another "part" of the observation space (you add an information to the gym space) |
They can all be used like:
import grid2op
env_name = ...
env = grid2op.make(env_name)
from grid2op.gym_compat import GymEnv, BoxGymObsSpace
gym_env = GymEnv(env)
gym_env.observation_space = BoxGymObsSpace(gym_env.init_env)
gym_env.action_space = MultiDiscreteActSpace(gym_env.init_env)
We encourage you to visit the documentation for more information on how to use these classes. Each offer different possible customization.
Starting from grid2op 1.9.1 we introduced the compatibility with gymnasium package (the replacement of the gym package that will no longer be maintained).
By default, if gymnasium is installed on your machine, all classes from the grid2op.gym_compat module will inherit from gymnasium. That is GymEnv
will be inherit from gymnasium.Env(and not gym.Env), GymActionSpace
will inherit from gymnasium.spaces.Dict (and not from gym.spaces.Dict) etc.
But we wanted to maintain Backward compatibility. It is ensured in two different ways:
- if you have both gymnasium and gym installed on your machine, you can choose which "framework" you want to use by explicitly using the right grid2op class. For example, if you want a gym environment (inheriting from gym.Env) you can use
GymEnv_Modern`and if you want to explicitly stay in `gymnasium
you can useGymnasiumEnv
- if you don't want to have gymnasium and only gym is installed then the default grid2op class will stay in the gym eco system. In this case, gym.Env will be
GymEnv_Modern
and all the code previously written will work exactly as before.
Note
As you understood if you want to keep the behaviour of grid2op prior to 1.9.1 the simplest solution would be not to install gymnasium at all.
If however you want to benefit from the latest gymnasium package, you can keep the previous code you have and simply install gymnasium. All classes defined there will still be defined and you will be able to use gymnasium transparently.
The table bellow summarize the correspondance between the default classes and the classes specific to gymnasium / gym:
Default class | Class with gymnasium | Class with gym |
---|---|---|
BaseGymAttrConverter |
BaseGymnasiumAttrConverter |
BaseLegacyGymAttrConverter |
BoxGymActSpace |
BoxGymnasiumActSpace |
BoxLegacyGymActSpace |
BoxGymObsSpace |
BoxGymnasiumObsSpace |
BoxLegacyGymObsSpace |
ContinuousToDiscreteConverter |
ContinuousToDiscreteConverterGymnasium |
ContinuousToDiscreteConverterLegacyGym |
DiscreteActSpace |
DiscreteActSpaceGymnasium |
DiscreteActSpaceLegacyGym |
GymActionSpace |
GymnasiumActionSpace |
LegacyGymActionSpace |
GymObservationSpace |
GymnasiumObservationSpace |
LegacyGymObservationSpace |
_BaseGymSpaceConverter |
_BaseGymnasiumSpaceConverter |
_BaseLegacyGymSpaceConverter |
GymEnv |
GymnasiumEnv |
GymEnv_Modern / GymEnv_Legacy |
MultiToTupleConverter |
MultiToTupleConverterGymnasium |
MultiToTupleConverterLegacyGym |
MultiDiscreteActSpace |
MultiDiscreteActSpaceGymnasium |
MultiDiscreteActSpaceLegacyGym |
ScalerAttrConverter |
ScalerAttrConverterGymnasium |
ScalerAttrConverterLegacyGym |
TODO
Any contribution is welcome here
Any contribution is welcome here too (-:
This usually takes the form of an error with XXX_env_name (eg CompleteObservation_l2rpn_wcci_2022) is not serializable.
This is because grid2op will (to save computation time) generate some classes (the classes themseleves) on the fly, once the environment is loaded. And unfortunately, pickle module is not always able to process these (meta) data.
Try to first create (automatically!) the files containing the description of the classes used by your environment (for example):
from grid2op import make
from grid2op.Reward import RedispReward
from lightsim2grid import LightSimBackend
env_name = 'l2rpn_wcci_2022'
backend_class = LightSimBackend
env = make(env_name, reward_class=RedispReward, backend=backend_class())
env.generate_classes()
Note
This piece of code is to do once (each time you change the backend or the env name)
And then proceed as usual by loading the grid2op environment with the key-word experimental_read_from_local_dir
from grid2op import make
from grid2op.Reward import RedispReward
from lightsim2grid import LightSimBackend
env_name = 'l2rpn_wcci_2022'
backend_class = LightSimBackend
env = make(env_name, reward_class=RedispReward, backend=backend_class(),
experimental_read_from_local_dir=True)
# do whatever
Often encountered with ray[rllib] this is due to a technical aspect (slack bus) of the grid which may cause issue with gen_p being above / bellow pmin / pmax for certain generators.
You can get rid of it by modifying the observation space and "remove" the low / high values on pmin and pmax:
# we suppose you already have an observation space
self.observation_space["gen_p"].low[:] = -np.inf
self.observation_space["gen_p"].high[:] = np.inf
grid2op.gym_compat
grid2op.gym_compat.gym_space_converter._BaseGymSpaceConverter