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gym_obs_space.py
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/
gym_obs_space.py
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# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)
# See AUTHORS.txt
# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0.
# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file,
# you can obtain one at http://mozilla.org/MPL/2.0/.
# SPDX-License-Identifier: MPL-2.0
# This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems.
import numpy as np
from gym import spaces
from grid2op.Environment import Environment, MultiMixEnvironment, BaseMultiProcessEnvironment
from grid2op.gym_compat.gym_space_converter import _BaseGymSpaceConverter
from grid2op.Observation import BaseObservation
from grid2op.dtypes import dt_int, dt_bool, dt_float
from grid2op.gym_compat.base_gym_attr_converter import BaseGymAttrConverter
class GymObservationSpace(_BaseGymSpaceConverter):
"""
This class allows to transform the observation space into a gym space.
Gym space will be a :class:`gym.spaces.Dict` with the keys being the different attributes
of the grid2op observation. All attributes are used.
Note that gym space converted with this class should be seeded independently. It is NOT seeded
when calling :func:`grid2op.Environment.Environment.seed`.
Examples
--------
Converting an observation space is fairly straightforward:
.. code-block:: python
import grid2op
from grid2op.Converter import GymObservationSpace
env = grid2op.make()
gym_observation_space = GymObservationSpace(env.observation_space)
# and now gym_observation_space is a `gym.spaces.Dict` representing the observation space
# you can "convert" the grid2op observation to / from this space with:
grid2op_obs = env.reset()
same_gym_obs = gym_observation_space.to_gym(grid2op_obs)
# the conversion from gym_obs to grid2op obs is feasible, but i don't imagine
# a situation where it is useful. And especially, you will not be able to
# use "obs.simulate" for the observation converted back from this gym action.
"""
def __init__(self, env, dict_variables=None):
if not isinstance(env, (Environment, MultiMixEnvironment, BaseMultiProcessEnvironment)):
raise RuntimeError("GymActionSpace must be created with an Environment of an ActionSpace (or a Converter)")
self._init_env = env
self.initial_obs_space = self._init_env.observation_space
dict_ = {} # will represent the gym.Dict space
if dict_variables is None:
dict_variables = {}
self._fill_dict_obs_space(dict_,
env.observation_space,
env.parameters,
env._oppSpace,
dict_variables)
_BaseGymSpaceConverter.__init__(self, dict_, dict_variables=dict_variables)
def reencode_space(self, key, fun):
"""
This function is used to reencode the observation space. For example, it can be used to scale
the observation into values close to 0., it can also be used to encode continuous variables into
discrete variables or the other way around etc.
Basically, it's a tool that lets you define your own observation space (there is the same for
the action space)
Parameters
----------
key: ``str``
Which part of the observation space you want to study
fun: :class:`BaseGymAttrConverter`
Put `None` to deactive the feature (it will be hided from the observation space)
It can also be a `BaseGymAttrConverter`. See the example for more information.
Returns
-------
self:
The current instance, to be able to chain these calls
Notes
------
It modifies the observation space. We highly recommend to set it up at the beginning of your script
and not to modify it afterwards
'fun' should be deep copiable (meaning that if `copy.deepcopy(fun)` is called, then it does not crash
If an attribute has been ignored, for example by :func`GymEnv.keep_only_obs_attr`
or and is now present here, it will be re added in the final observation
"""
my_dict = self.get_dict_encoding()
if fun is not None and not isinstance(fun, BaseGymAttrConverter):
raise RuntimeError("Impossible to initialize a converter with a function of type {}".format(type(fun)))
if fun is not None and not fun.is_init_space():
if key in my_dict:
fun.initialize_space(my_dict[key])
elif key in self.spaces:
fun.initialize_space(self.spaces[key])
else:
raise RuntimeError(f"Impossible to find key {key} in your observation space")
my_dict[key] = fun
res = GymObservationSpace(self._init_env, my_dict)
return res
def _fill_dict_obs_space(self, dict_, observation_space, env_params, opponent_space,
dict_variables={}):
for attr_nm, sh, dt in zip(observation_space.attr_list_vect,
observation_space.shape,
observation_space.dtype):
if sh == 0:
continue
my_type = None
shape = (sh,)
if attr_nm in dict_variables:
# case where the user specified a dedicated encoding
if dict_variables[attr_nm] is None:
# none is by default to disable this feature
continue
my_type = dict_variables[attr_nm].my_space
elif dt == dt_int:
# discrete observation space
if attr_nm == "year":
my_type = spaces.Discrete(n=2100)
elif attr_nm == "month":
my_type = spaces.Discrete(n=13)
elif attr_nm == "day":
my_type = spaces.Discrete(n=32)
elif attr_nm == "hour_of_day":
my_type = spaces.Discrete(n=24)
elif attr_nm == "minute_of_hour":
my_type = spaces.Discrete(n=60)
elif attr_nm == "day_of_week":
my_type = spaces.Discrete(n=8)
elif attr_nm == "topo_vect":
my_type = spaces.Box(low=-1, high=2, shape=shape, dtype=dt)
elif attr_nm == "time_before_cooldown_line":
my_type = spaces.Box(low=0,
high=max(env_params.NB_TIMESTEP_COOLDOWN_LINE,
env_params.NB_TIMESTEP_RECONNECTION,
opponent_space.attack_duration
),
shape=shape,
dtype=dt)
elif attr_nm == "time_before_cooldown_sub":
my_type = spaces.Box(low=0,
high=env_params.NB_TIMESTEP_COOLDOWN_SUB,
shape=shape,
dtype=dt)
elif attr_nm == "duration_next_maintenance" or attr_nm == "time_next_maintenance":
# can be -1 if no maintenance, otherwise always positive
my_type = self._generic_gym_space(dt, sh, low=-1)
elif dt == dt_bool:
# boolean observation space
my_type = self._boolean_type(sh)
else:
# continuous observation space
low = float("-inf")
high = float("inf")
shape = (sh,)
SpaceType = spaces.Box
if attr_nm == "gen_p" or attr_nm == "gen_p_before_curtail":
low = observation_space.gen_pmin
high = observation_space.gen_pmax * 1.2 # because of the slack bus... # TODO
shape = None
elif attr_nm == "gen_v" or attr_nm == "load_v" or attr_nm == "v_or" or attr_nm == "v_ex":
# voltages can't be negative
low = 0.
elif attr_nm == "a_or" or attr_nm == "a_ex" or attr_nm == "rho":
# amps can't be negative
low = 0.
elif attr_nm == "target_dispatch" or attr_nm == "actual_dispatch":
# TODO check that to be sure
low = np.minimum(observation_space.gen_pmin,
-observation_space.gen_pmax)
high = np.maximum(-observation_space.gen_pmin,
+observation_space.gen_pmax)
elif attr_nm == "storage_power" or attr_nm == "storage_power_target":
low = - observation_space.storage_max_p_prod
high = observation_space.storage_max_p_absorb
elif attr_nm == "storage_charge":
low = np.zeros(observation_space.n_storage, dtype=dt_float)
high = observation_space.storage_Emax
elif attr_nm == "curtailment" or attr_nm == "curtailment_limit":
low = 0.
high = 1.0
# curtailment, curtailment_limit, gen_p_before_curtail
my_type = SpaceType(low=low, high=high, shape=shape, dtype=dt)
if my_type is None:
# if nothing has been found in the specific cases above
my_type = self._generic_gym_space(dt, sh)
dict_[attr_nm] = my_type
def from_gym(self, gymlike_observation: spaces.dict.OrderedDict) -> BaseObservation:
"""
This function convert the gym-like representation of an observation to a grid2op observation.
Parameters
----------
gymlike_observation: :class:`gym.spaces.dict.OrderedDict`
The observation represented as a gym ordered dict
Returns
-------
grid2oplike_observation: :class:`grid2op.Observation.BaseObservation`
The corresponding grid2op observation
"""
res = self.initial_obs_space.get_empty_observation()
for k, v in gymlike_observation.items():
res._assign_attr_from_name(k, v)
return res
def to_gym(self, grid2op_observation: BaseObservation) -> spaces.dict.OrderedDict:
"""
Convert a grid2op observation into a gym ordered dict.
Parameters
----------
grid2op_observation: :class:`grid2op.Observation.BaseObservation`
The observation represented as a grid2op observation
Returns
-------
gymlike_observation: :class:`gym.spaces.dict.OrderedDict`
The corresponding gym ordered dict
"""
return self._base_to_gym(self.spaces.keys(),
grid2op_observation,
dtypes={k: self.spaces[k].dtype for k in self.spaces}
)