def shape2space(space: ObservationSpace) -> Space:
"""Convert an ObservationSpace description into a gym Space."""
def make_box(scalar_range_list, dtype, defaults):
bounds = [scalar_range2tuple(r, defaults) for r in scalar_range_list]
return Box(
low=np.array([b[0] for b in bounds], dtype=dtype),
high=np.array([b[1] for b in bounds], dtype=dtype),
dtype=dtype,
)
def make_seq(scalar_range, dtype, defaults):
return Sequence(
size_range=scalar_range2tuple(scalar_range, defaults),
dtype=dtype,
opaque_data_format=space.opaque_data_format,
)
shape_type = space.WhichOneof("shape")
if shape_type == "int64_range_list":
return make_box(
space.int64_range_list.range,
np.int64,
(np.iinfo(np.int64).min, np.iinfo(np.int64).max),
)
elif shape_type == "double_range_list":
return make_box(space.double_range_list.range, np.float64,
(-np.inf, np.inf))
elif shape_type == "string_size_range":
return make_seq(space.string_size_range, str, (0, None))
elif shape_type == "binary_size_range":
return make_seq(space.binary_size_range, bytes, (0, None))
else:
raise TypeError(f"Cannot determine shape of ObservationSpace: {space}")
def from_proto(cls, index: int, proto: ObservationSpace):
"""Construct a space from an ObservationSpace message."""
shape_type = proto.WhichOneof("shape")
def make_box(scalar_range_list, dtype, defaults):
bounds = [
scalar_range2tuple(r, defaults) for r in scalar_range_list
]
return Box(
low=np.array([b[0] for b in bounds], dtype=dtype),
high=np.array([b[1] for b in bounds], dtype=dtype),
dtype=dtype,
)
def make_seq(scalar_range, dtype, defaults):
return Sequence(
size_range=scalar_range2tuple(scalar_range, defaults),
dtype=dtype,
opaque_data_format=proto.opaque_data_format,
)
# Translate from protocol buffer specification to python. There are
# three variables to derive: 'space', the gym.Space instance describing
# the space. 'cb' is a callback that translates from an Observation
# message to a python type. and 'to_string' is a callback that
# translates from a python type to a string for printing.
if proto.opaque_data_format == "json://networkx/MultiDiGraph":
# TODO(cummins): Add a Graph space.
space = make_seq(proto.string_size_range, str, (0, None))
def cb(observation):
return nx.readwrite.json_graph.node_link_graph(json.loads(
observation.string_value),
multigraph=True,
directed=True)
def to_string(observation):
return json.dumps(
nx.readwrite.json_graph.node_link_data(observation),
indent=2)
elif proto.opaque_data_format == "json://":
space = make_seq(proto.string_size_range, str, (0, None))
def cb(observation):
return json.loads(observation.string_value)
def to_string(observation):
return json.dumps(observation, indent=2)
elif shape_type == "int64_range_list":
space = make_box(
proto.int64_range_list.range,
np.int64,
(np.iinfo(np.int64).min, np.iinfo(np.int64).max),
)
def cb(observation):
return np.array(observation.int64_list.value, dtype=np.int64)
to_string = str
elif shape_type == "double_range_list":
space = make_box(proto.double_range_list.range, np.float64,
(-np.inf, np.inf))
def cb(observation):
return np.array(observation.double_list.value,
dtype=np.float64)
to_string = str
elif shape_type == "string_size_range":
space = make_seq(proto.string_size_range, str, (0, None))
def cb(observation):
return observation.string_value
to_string = str
elif shape_type == "binary_size_range":
space = make_seq(proto.binary_size_range, bytes, (0, None))
def cb(observation):
return observation.binary_value
to_string = str
else:
raise TypeError(
f"Cannot determine shape of ObservationSpace: {proto}")
return cls(
id=proto.name,
index=index,
space=space,
cb=cb,
to_string=to_string,
deterministic=proto.deterministic,
platform_dependent=proto.platform_dependent,
default_value=cb(proto.default_value),
)