call on superclass for obs wrapper

alr_envs/alr/classic_control/base_reacher/base_reacher.py

@@ -1,10 +1,11 @@
-from typing import Iterable, Union
 from abc import ABC, abstractmethod
+from typing import Union
+
 import gym
-import matplotlib.pyplot as plt
 import numpy as np
 from gym import spaces
 from gym.utils import seeding
+
 from alr_envs.alr.classic_control.utils import intersect
 
 

alr_envs/alr/classic_control/hole_reacher/mp_wrapper.py

@@ -1,27 +0,0 @@
-from typing import Tuple, Union
-
-import numpy as np
-
-from alr_envs.mp.raw_interface_wrapper import RawInterfaceWrapper
-
-
-class MPWrapper(RawInterfaceWrapper):
-
-    def get_context_mask(self):
-        return np.hstack([
-            [self.env.random_start] * self.env.n_links,  # cos
-            [self.env.random_start] * self.env.n_links,  # sin
-            [self.env.random_start] * self.env.n_links,  # velocity
-            [self.env.initial_width is None],  # hole width
-            # [self.env.hole_depth is None],  # hole depth
-            [True] * 2,  # x-y coordinates of target distance
-            [False]  # env steps
-        ])
-
-    @property
-    def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
-        return self.env.current_pos
-
-    @property
-    def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
-        return self.env.current_vel

alr_envs/alr/classic_control/viapoint_reacher/mp_wrapper.py

@@ -6,8 +6,8 @@ from alr_envs.mp.raw_interface_wrapper import RawInterfaceWrapper
 
 
 class MPWrapper(RawInterfaceWrapper):
-    @property
+
-    def context_mask(self) -> np.ndarray:
+    def context_mask(self):
         return np.hstack([
             [self.env.random_start] * self.env.n_links,  # cos
             [self.env.random_start] * self.env.n_links,  # sin
@@ -25,10 +25,6 @@ class MPWrapper(RawInterfaceWrapper):
     def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
         return self.env.current_vel
 
-    @property
-    def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
-        raise ValueError("Goal position is not available and has to be learnt based on the environment.")
-
     @property
     def dt(self) -> Union[float, int]:
         return self.env.dt

alr_envs/alr/classic_control/viapoint_reacher/new_mp_wrapper.py

@@ -1,30 +0,0 @@
-from typing import Tuple, Union
-
-import numpy as np
-
-from alr_envs.mp.raw_interface_wrapper import RawInterfaceWrapper
-
-
-class MPWrapper(RawInterfaceWrapper):
-
-    def context_mask(self):
-        return np.hstack([
-            [self.env.random_start] * self.env.n_links,  # cos
-            [self.env.random_start] * self.env.n_links,  # sin
-            [self.env.random_start] * self.env.n_links,  # velocity
-            [self.env.initial_via_target is None] * 2,  # x-y coordinates of via point distance
-            [True] * 2,  # x-y coordinates of target distance
-            [False]  # env steps
-        ])
-
-    @property
-    def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
-        return self.env.current_pos
-
-    @property
-    def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
-        return self.env.current_vel
-
-    @property
-    def dt(self) -> Union[float, int]:
-        return self.env.dt

alr_envs/alr/mujoco/reacher/__init__.py

@@ -1,2 +1,2 @@
 from .mp_wrapper import MPWrapper
-from .new_mp_wrapper import MPWrapper as NewMPWrapper
+from .mp_wrapper import MPWrapper as NewMPWrapper

alr_envs/alr/mujoco/reacher/mp_wrapper.py

@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Union, Tuple
 
 import numpy as np
 
@@ -8,37 +8,21 @@ from alr_envs.mp.raw_interface_wrapper import RawInterfaceWrapper
 class MPWrapper(RawInterfaceWrapper):
 
     @property
-    def context_mask(self) -> np.ndarray:
+    def context_mask(self):
         return np.concatenate([
-            [False] * self.n_links,  # cos
+            [False] * self.env.n_links,  # cos
-            [False] * self.n_links,  # sin
+            [False] * self.env.n_links,  # sin
             [True] * 2,  # goal position
-            [False] * self.n_links,  # angular velocity
+            [False] * self.env.n_links,  # angular velocity
             [False] * 3,  # goal distance
             # self.get_body_com("target"),  # only return target to make problem harder
             [False],  # step
         ])
 
-    # @property
+    @property
-    # def active_obs(self):
+    def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
-    #     return np.concatenate([
+        return self.env.sim.data.qpos.flat[:self.env.n_links]
-    #         [True] * self.n_links,  # cos, True
-    #         [True] * self.n_links,  # sin, True
-    #         [True] * 2,  # goal position
-    #         [True] * self.n_links,  # angular velocity, True
-    #         [True] * 3,  # goal distance
-    #         # self.get_body_com("target"),  # only return target to make problem harder
-    #         [False],  # step
-    #     ])
 
     @property
-    def current_vel(self) -> Union[float, int, np.ndarray]:
+    def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
-        return self.sim.data.qvel.flat[:self.n_links]
+        return self.env.sim.data.qvel.flat[:self.env.n_links]
-
-    @property
-    def current_pos(self) -> Union[float, int, np.ndarray]:
-        return self.sim.data.qpos.flat[:self.n_links]
-
-    @property
-    def dt(self) -> Union[float, int]:
-        return self.env.dt

alr_envs/alr/mujoco/reacher/new_mp_wrapper.py

@@ -1,28 +0,0 @@
-from alr_envs.mp.black_box_wrapper import BlackBoxWrapper
-from typing import Union, Tuple
-import numpy as np
-
-from alr_envs.mp.raw_interface_wrapper import RawInterfaceWrapper
-
-
-class MPWrapper(RawInterfaceWrapper):
-
-    @property
-    def context_mask(self):
-        return np.concatenate([
-            [False] * self.env.n_links,  # cos
-            [False] * self.env.n_links,  # sin
-            [True] * 2,  # goal position
-            [False] * self.env.n_links,  # angular velocity
-            [False] * 3,  # goal distance
-            # self.get_body_com("target"),  # only return target to make problem harder
-            [False],  # step
-        ])
-
-    @property
-    def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
-        return self.env.sim.data.qpos.flat[:self.env.n_links]
-
-    @property
-    def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
-        return self.env.sim.data.qvel.flat[:self.env.n_links]

alr_envs/mp/black_box_wrapper.py

@@ -11,7 +11,7 @@ from alr_envs.mp.raw_interface_wrapper import RawInterfaceWrapper
 from alr_envs.utils.utils import get_numpy
 
 
-class BlackBoxWrapper(gym.ObservationWrapper, ABC):
+class BlackBoxWrapper(gym.ObservationWrapper):
 
     def __init__(self,
                  env: RawInterfaceWrapper,
@@ -34,9 +34,8 @@ class BlackBoxWrapper(gym.ObservationWrapper, ABC):
             reward_aggregation: function that takes the np.ndarray of step rewards as input and returns the trajectory
                 reward, default summation over all values.
         """
-        super().__init__()
+        super().__init__(env)
 
-        self.env = env
         self.duration = duration
         self.learn_sub_trajectories = learn_sub_trajectories
         self.replanning_schedule = replanning_schedule