def test_creation(self):
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("l2rpn_wcci_2020", test=True) as env:
# test i can create
converter = self.init_converter(env)
act_space = GymActionSpace(env=env, converter=converter)
act_space.sample()
def test_json(self):
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("l2rpn_wcci_2020", test=True) as env:
# test i can create
obs_space = GymObservationSpace(env)
act_space = GymActionSpace(env)
obs_space.seed(0)
act_space.seed(0)
self._aux_test_json(obs_space)
self._aux_test_json(act_space)
def test_creation(self):
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make(self.get_env_name(), test=True) as env:
# test i can create
obs_space = GymObservationSpace(env)
act_space = GymActionSpace(env)
def _init_gym_converter(self):
if self.__gym_action_space is None:
# lazy import
from gym import spaces
from grid2op.gym_compat import GymActionSpace
# i do that not to duplicate the code of the low / high bounds
gym_action_space = GymActionSpace(self.init_action_space)
low = tuple()
high = tuple()
order_gym = []
dtypes = []
shapes = []
sizes = []
prev = 0
for k, v in gym_action_space.spaces.items():
order_gym.append(k)
dtypes.append(v.dtype)
if isinstance(v, spaces.MultiBinary):
low += tuple([0 for _ in range(v.n)])
high += tuple([1 for _ in range(v.n)])
my_size = v.n
elif isinstance(v, spaces.Box):
low += tuple(v.low)
high += tuple(v.high)
my_size = v.low.shape[0]
else:
raise RuntimeError(
"Impossible to convert this converter to gym. Type {} of data "
"encountered while only MultiBinary and Box are supported for now."
)
shapes.append(my_size)
sizes.append(np.arange(my_size) + prev)
prev += my_size
self.__gym_action_space = gym_action_space
my_type = spaces.Box(low=np.array(low),
high=np.array(high),
dtype=dt_float)
order_me = []
_order_gym_2_me = np.zeros(my_type.shape[0], dtype=dt_int) - 1
_order_me_2_gym = np.zeros(my_type.shape[0], dtype=dt_int) - 1
for el in self.init_action_space.attr_list_vect:
order_me.append(GymActionSpace.keys_grid2op_2_human[el])
prev = 0
order_gym = list(gym_action_space.spaces.keys())
for id_me, nm_attr in enumerate(order_me):
id_gym = order_gym.index(nm_attr)
index_me = np.arange(shapes[id_gym]) + prev
_order_gym_2_me[sizes[id_gym]] = index_me
_order_me_2_gym[index_me] = sizes[id_gym]
# self.__order_gym_2_me[this_gym_ind] = sizes[id_me]
prev += shapes[id_gym]
self.__order_gym_2_me = _order_gym_2_me
self.__order_me_2_gym = _order_me_2_gym
self.__dict_space = {"action": my_type}
self.__order_gym = order_gym
self.__dtypes_gym = dtypes
self.__shapes_gym = shapes
def test_creation(self):
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
if self.get_env_path() is not None:
env_path_or_name = os.path.join(self.get_env_path(),
self.get_env_name())
else:
env_path_or_name = self.get_env_name()
with make(env_path_or_name, test=True) as env:
# test i can create
obs_space = GymObservationSpace(env)
act_space = GymActionSpace(env)
def test_to_from_gym_act(self):
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("l2rpn_wcci_2020", test=True) as env:
converter = self.init_converter(env)
act_space = GymActionSpace(env=env, converter=converter)
act_space.seed(0)
converter.seed(0)
gym_act = act_space.sample()
act = act_space.from_gym(gym_act)
self._aux_test_json(act_space, gym_act)
gym_act2 = act_space.to_gym(act)
act2 = act_space.from_gym(gym_act2)
g2op_act = converter.convert_act(act)
g2op_act2 = converter.convert_act(act2)
assert g2op_act == g2op_act2
act_space.seed(0)
for i in range(10):
gym_act = act_space.sample()
act = act_space.from_gym(gym_act)
self._aux_test_json(act_space, gym_act)
gym_act2 = act_space.to_gym(act)
act2 = act_space.from_gym(gym_act2)
g2op_act = converter.convert_act(act)
g2op_act2 = converter.convert_act(act2)
assert g2op_act == g2op_act2
def test_to_from_gym_act(self):
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("l2rpn_wcci_2020", test=True) as env:
act_space = GymActionSpace(env)
act = env.action_space()
gym_act = act_space.to_gym(act)
self._aux_test_json(act_space, gym_act)
assert act_space.contains(gym_act)
act2 = act_space.from_gym(gym_act)
assert act == act2
act_space.seed(0)
for i in range(10):
gym_act = act_space.sample()
act = act_space.from_gym(gym_act)
self._aux_test_json(act_space, gym_act)
gym_act2 = act_space.to_gym(act)
act2 = act_space.from_gym(gym_act2)
assert act == act2