matched file structure of classic control with other tasks

alr_envs/classic_control/__init__.py

@@ -1,3 +1,3 @@
-from alr_envs.classic_control.simple_reacher import SimpleReacherEnv
-from alr_envs.classic_control.viapoint_reacher import ViaPointReacher
-from alr_envs.classic_control.hole_reacher import HoleReacherEnv
+from alr_envs.classic_control.viapoint_reacher.viapoint_reacher import ViaPointReacher
+from alr_envs.classic_control.simple_reacher.simple_reacher import SimpleReacherEnv
+from alr_envs.classic_control.hole_reacher.hole_reacher import HoleReacherEnv

alr_envs/classic_control/hole_reacher/hole_reacher.py

@@ -7,8 +7,6 @@ from gym.utils import seeding
 from matplotlib import patches
 
 from alr_envs.classic_control.utils import check_self_collision
-from mp_env_api.envs.mp_env import MpEnv
-from mp_env_api.envs.mp_env_wrapper import MPEnvWrapper
 
 
 class HoleReacherEnv(gym.Env):
@@ -289,29 +287,3 @@ class HoleReacherEnv(gym.Env):
         super().close()
         if self.fig is not None:
             plt.close(self.fig)
-
-
-class HoleReacherMPWrapper(MPEnvWrapper):
-    @property
-    def active_obs(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.hole_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 start_pos(self) -> Union[float, int, np.ndarray]:
-        return self._start_pos
-
-    @property
-    def goal_pos(self) -> Union[float, int, np.ndarray]:
-        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/classic_control/hole_reacher/hole_reacher_mp_wrapper.py

@@ -0,0 +1,31 @@
+from typing import Union
+
+import numpy as np
+
+from mp_env_api.envs.mp_env_wrapper import MPEnvWrapper
+
+
+class HoleReacherMPWrapper(MPEnvWrapper):
+    @property
+    def active_obs(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.hole_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 start_pos(self) -> Union[float, int, np.ndarray]:
+        return self._start_pos
+
+    @property
+    def goal_pos(self) -> Union[float, int, np.ndarray]:
+        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/classic_control/simple_reacher/simple_reacher.py

@@ -6,8 +6,6 @@ import numpy as np
 from gym import spaces
 from gym.utils import seeding
 
-from mp_env_api.envs.mp_env_wrapper import MPEnvWrapper
-
 
 class SimpleReacherEnv(gym.Env):
     """
@@ -187,27 +185,3 @@ class SimpleReacherEnv(gym.Env):
     @property
     def end_effector(self):
         return self._joints[self.n_links].T
-
-
-class SimpleReacherMPWrapper(MPEnvWrapper):
-    @property
-    def active_obs(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
-            [True] * 2,  # x-y coordinates of target distance
-            [False]  # env steps
-        ])
-
-    @property
-    def start_pos(self):
-        return self._start_pos
-
-    @property
-    def goal_pos(self):
-        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/classic_control/simple_reacher/simple_reacher_mp_wrapper.py

@@ -0,0 +1,29 @@
+from typing import Union
+
+import numpy as np
+
+from mp_env_api.envs.mp_env_wrapper import MPEnvWrapper
+
+
+class SimpleReacherMPWrapper(MPEnvWrapper):
+    @property
+    def active_obs(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
+            [True] * 2,  # x-y coordinates of target distance
+            [False]  # env steps
+        ])
+
+    @property
+    def start_pos(self):
+        return self._start_pos
+
+    @property
+    def goal_pos(self):
+        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/classic_control/utils.py

@@ -10,7 +10,7 @@ def intersect(A, B, C, D):
 
 
 def check_self_collision(line_points):
-    "Checks whether line segments and intersect"
+    """Checks whether line segments and intersect"""
     for i, line1 in enumerate(line_points):
         for line2 in line_points[i + 2:, :, :]:
             if intersect(line1[0], line1[-1], line2[0], line2[-1]):

alr_envs/classic_control/viapoint_reacher/viapoint_reacher.py

@@ -6,8 +6,6 @@ import numpy as np
 from gym.utils import seeding
 
 from alr_envs.classic_control.utils import check_self_collision
-from mp_env_api.envs.mp_env import MpEnv
-from mp_env_api.envs.mp_env_wrapper import MPEnvWrapper
 
 
 class ViaPointReacher(gym.Env):
@@ -282,27 +280,3 @@ class ViaPointReacher(gym.Env):
     def close(self):
         if self.fig is not None:
             plt.close(self.fig)
-
-
-class ViaPointReacherMPWrapper(MPEnvWrapper):
-    @property
-    def active_obs(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.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 start_pos(self) -> Union[float, int, np.ndarray]:
-        return self._start_pos
-
-    @property
-    def goal_pos(self) -> Union[float, int, np.ndarray]:
-        raise ValueError("Goal position is not available and has to be learnt based on the environment.")
-
-    def dt(self) -> Union[float, int]:
-        return self.env.dt

alr_envs/classic_control/viapoint_reacher/viapoint_reacher_mp_wrapper.py

@@ -0,0 +1,29 @@
+from typing import Union
+
+import numpy as np
+
+from mp_env_api.envs.mp_env_wrapper import MPEnvWrapper
+
+
+class ViaPointReacherMPWrapper(MPEnvWrapper):
+    @property
+    def active_obs(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.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 start_pos(self) -> Union[float, int, np.ndarray]:
+        return self._start_pos
+
+    @property
+    def goal_pos(self) -> Union[float, int, np.ndarray]:
+        raise ValueError("Goal position is not available and has to be learnt based on the environment.")
+
+    def dt(self) -> Union[float, int]:
+        return self.env.dt

alr_envs/mujoco/alr_mujoco_env.py

@@ -98,14 +98,14 @@ class AlrMujocoEnv(PositionalEnv, AlrEnv):
     @property
     def start_pos(self):
         """
-        Start position of the agent, for example joint angles of a Panda robot. Necessary for MP wrapped envs.
+        Start position of the agent, for example joint angles of a Panda robot. Necessary for MP wrapped simple_reacher.
         """
         return self._start_pos
 
     @property
     def start_vel(self):
         """
-        Start velocity of the agent. Necessary for MP wrapped envs.
+        Start velocity of the agent. Necessary for MP wrapped simple_reacher.
         """
         return self._start_vel
 

alr_envs/mujoco/gym_table_tennis/utils/util.py

@@ -39,7 +39,7 @@ def config_save(dir_path, config):
 
 
 def change_kp_in_xml(kp_list,
-                     model_path="/home/zhou/slow/table_tennis_rl/simulation/gymTableTennis/gym_table_tennis/envs/robotics/assets/table_tennis/right_arm_actuator.xml"):
+                     model_path="/home/zhou/slow/table_tennis_rl/simulation/gymTableTennis/gym_table_tennis/simple_reacher/robotics/assets/table_tennis/right_arm_actuator.xml"):
     tree = ET.parse(model_path)
     root = tree.getroot()
     # for actuator in root.find("actuator"):