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lighthouse_sensors.py
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lighthouse_sensors.py
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import itertools
from typing import Any, Dict, Optional, Tuple, Sequence
import gym
import numpy as np
import pandas as pd
import patsy
from allenact.base_abstractions.sensor import Sensor, prepare_locals_for_super
from allenact.base_abstractions.task import Task
from allenact_plugins.lighthouse_plugin.lighthouse_environment import (
LightHouseEnvironment,
)
def get_corner_observation(
env: LightHouseEnvironment,
view_radius: int,
view_corner_offsets: Optional[np.array],
):
if view_corner_offsets is None:
view_corner_offsets = view_radius * (2 * (env.world_corners > 0) - 1)
world_corners_offset = env.world_corners + env.world_radius
multidim_view_corner_indices = np.clip(
np.reshape(env.current_position, (1, -1))
+ view_corner_offsets
+ env.world_radius,
a_min=0,
a_max=2 * env.world_radius,
)
flat_view_corner_indices = np.ravel_multi_index(
np.transpose(multidim_view_corner_indices), env.world_tensor.shape
)
view_values = env.world_tensor.reshape(-1)[flat_view_corner_indices]
last_action = 2 * env.world_dim if env.last_action is None else env.last_action
on_border_bools = np.concatenate(
(
env.current_position == env.world_radius,
env.current_position == -env.world_radius,
),
axis=0,
)
if last_action == 2 * env.world_dim or on_border_bools[last_action]:
on_border_value = last_action
elif on_border_bools.any():
on_border_value = np.argwhere(on_border_bools).reshape(-1)[0]
else:
on_border_value = 2 * env.world_dim
seen_mask = np.array(env.closest_distance_to_corners <= view_radius, dtype=int)
seen_corner_values = (
env.world_tensor.reshape(-1)[
np.ravel_multi_index(
np.transpose(world_corners_offset), env.world_tensor.shape
)
]
* seen_mask
)
return np.concatenate(
(
seen_corner_values + view_values * (1 - seen_mask),
[on_border_value, last_action],
),
axis=0,
out=np.zeros((seen_corner_values.shape[0] + 2,), dtype=np.float32,),
)
class CornerSensor(Sensor[LightHouseEnvironment, Any]):
def __init__(
self,
view_radius: int,
world_dim: int,
uuid: str = "corner_fixed_radius",
**kwargs: Any
):
self.view_radius = view_radius
self.world_dim = world_dim
self.view_corner_offsets: Optional[np.ndarray] = None
observation_space = self._get_observation_space()
super().__init__(**prepare_locals_for_super(locals()))
def _get_observation_space(self):
return gym.spaces.Box(
low=min(LightHouseEnvironment.SPACE_LEVELS),
high=max(LightHouseEnvironment.SPACE_LEVELS),
shape=(2 ** self.world_dim + 2,),
dtype=int,
)
def get_observation(
self,
env: LightHouseEnvironment,
task: Optional[Task],
*args: Any,
**kwargs: Any
) -> Any:
if self.view_corner_offsets is None:
self.view_corner_offsets = self.view_radius * (
2 * (env.world_corners > 0) - 1
)
return get_corner_observation(
env=env,
view_radius=self.view_radius,
view_corner_offsets=self.view_corner_offsets,
)
class FactorialDesignCornerSensor(Sensor[LightHouseEnvironment, Any]):
_DESIGN_MAT_CACHE: Dict[Tuple, Any] = {}
def __init__(
self,
view_radius: int,
world_dim: int,
degree: int,
uuid: str = "corner_fixed_radius_categorical",
**kwargs: Any
):
self.view_radius = view_radius
self.world_dim = world_dim
self.degree = degree
if self.world_dim > 2:
raise NotImplementedError(
"When using the `FactorialDesignCornerSensor`,"
"`world_dim` must be <= 2 due to memory constraints."
"In the current implementation, creating the design"
"matrix in the `world_dim == 3` case would require"
"instantiating a matrix of size ~ 3Mx3M (9 trillion entries)."
)
self.view_corner_offsets: Optional[np.ndarray] = None
# self.world_corners_offset: Optional[List[typing.Tuple[int, ...]]] = None
self.corner_sensor = CornerSensor(self.view_radius, self.world_dim)
self.variables_and_levels = self._get_variables_and_levels(
world_dim=self.world_dim
)
self._design_mat_formula = self._create_formula(
variables_and_levels=self._get_variables_and_levels(
world_dim=self.world_dim
),
degree=self.degree,
)
self.single_row_df = pd.DataFrame(
data=[[0] * len(self.variables_and_levels)],
columns=[x[0] for x in self.variables_and_levels],
)
self._view_tuple_to_design_array: Dict[Tuple[int, ...], np.ndarray] = {}
(
design_matrix,
tuple_to_ind,
) = self._create_full_design_matrix_and_tuple_to_ind_dict(
variables_and_levels=tuple(self.variables_and_levels), degree=self.degree
)
self.design_matrix = design_matrix
self.tuple_to_ind = tuple_to_ind
observation_space = self._get_observation_space()
super().__init__(**prepare_locals_for_super(locals()))
def _get_observation_space(self):
return gym.spaces.Box(
low=min(LightHouseEnvironment.SPACE_LEVELS),
high=max(LightHouseEnvironment.SPACE_LEVELS),
shape=(
len(
self.view_tuple_to_design_array(
(0,) * len(self.variables_and_levels)
)
),
),
dtype=int,
)
def view_tuple_to_design_array(self, view_tuple: Tuple):
return np.array(
self.design_matrix[self.tuple_to_ind[view_tuple], :], dtype=np.float32
)
@classmethod
def output_dim(cls, world_dim: int):
return ((3 if world_dim == 1 else 4) ** (2 ** world_dim)) * (
2 * world_dim + 1
) ** 2
@classmethod
def _create_full_design_matrix_and_tuple_to_ind_dict(
cls, variables_and_levels: Sequence[Tuple[str, Sequence[int]]], degree: int
):
variables_and_levels = tuple((x, tuple(y)) for x, y in variables_and_levels)
key = (variables_and_levels, degree)
if key not in cls._DESIGN_MAT_CACHE:
all_tuples = [
tuple(x)
for x in itertools.product(
*[levels for _, levels in variables_and_levels]
)
]
tuple_to_ind = {}
for i, t in enumerate(all_tuples):
tuple_to_ind[t] = i
df = pd.DataFrame(
data=all_tuples,
columns=[var_name for var_name, _ in variables_and_levels],
)
cls._DESIGN_MAT_CACHE[key] = (
np.array(
1.0
* patsy.dmatrix(
cls._create_formula(
variables_and_levels=variables_and_levels, degree=degree
),
data=df,
),
dtype=bool,
),
tuple_to_ind,
)
return cls._DESIGN_MAT_CACHE[key]
@staticmethod
def _get_variables_and_levels(world_dim: int):
return (
[
("s{}".format(i), list(range(3 if world_dim == 1 else 4)))
for i in range(2 ** world_dim)
]
+ [("b{}".format(i), list(range(2 * world_dim + 1))) for i in range(1)]
+ [("a{}".format(i), list(range(2 * world_dim + 1))) for i in range(1)]
)
@classmethod
def _create_formula(
cls, variables_and_levels: Sequence[Tuple[str, Sequence[int]]], degree: int
):
def make_categorial(var_name, levels):
return "C({}, levels={})".format(var_name, levels)
if degree == -1:
return ":".join(
make_categorial(var_name, levels)
for var_name, levels in variables_and_levels
)
else:
return "({})**{}".format(
"+".join(
make_categorial(var_name, levels)
for var_name, levels in variables_and_levels
),
degree,
)
def get_observation(
self,
env: LightHouseEnvironment,
task: Optional[Task],
*args: Any,
**kwargs: Any
) -> Any:
kwargs["as_tuple"] = True
view_array = self.corner_sensor.get_observation(env, task, *args, **kwargs)
return self.view_tuple_to_design_array(tuple(view_array))