From 95e9b8be47d8d7d2a508082c43db80aa2d3e6fe5 Mon Sep 17 00:00:00 2001
From: ottofabian <fabi.otto@t-online.de>
Date: Wed, 12 May 2021 09:52:25 +0200
Subject: [PATCH 1/2] added MPEnv

---
 alr_envs/__init__.py                          |   2 +-
 alr_envs/classic_control/hole_reacher.py      | 195 +++++++++++-------
 alr_envs/mujoco/ball_in_a_cup/utils.py        |   4 +-
 alr_envs/mujoco/beerpong/utils.py             |   2 +-
 alr_envs/utils/legacy/utils.py                |  10 +-
 alr_envs/utils/make_env_helpers.py            |   4 +-
 alr_envs/utils/{wrapper => mps}/__init__.py   |   0
 .../utils/{wrapper => mps}/detpmp_wrapper.py  |  13 +-
 .../utils/{wrapper => mps}/dmp_wrapper.py     |  56 ++---
 alr_envs/utils/mps/mp_environments.py         |  33 +++
 alr_envs/utils/{wrapper => mps}/mp_wrapper.py |  49 +----
 11 files changed, 200 insertions(+), 168 deletions(-)
 rename alr_envs/utils/{wrapper => mps}/__init__.py (100%)
 rename alr_envs/utils/{wrapper => mps}/detpmp_wrapper.py (66%)
 rename alr_envs/utils/{wrapper => mps}/dmp_wrapper.py (53%)
 create mode 100644 alr_envs/utils/mps/mp_environments.py
 rename alr_envs/utils/{wrapper => mps}/mp_wrapper.py (68%)

diff --git a/alr_envs/__init__.py b/alr_envs/__init__.py
index 8e46fa9..fe32c0b 100644
--- a/alr_envs/__init__.py
+++ b/alr_envs/__init__.py
@@ -1,7 +1,7 @@
 from gym.envs.registration import register
 
 from alr_envs.stochastic_search.functions.f_rosenbrock import Rosenbrock
-# from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper
+# from alr_envs.utils.mps.dmp_wrapper import DmpWrapper
 
 # Mujoco
 
diff --git a/alr_envs/classic_control/hole_reacher.py b/alr_envs/classic_control/hole_reacher.py
index 3b382f9..45bc1c3 100644
--- a/alr_envs/classic_control/hole_reacher.py
+++ b/alr_envs/classic_control/hole_reacher.py
@@ -1,27 +1,34 @@
+from typing import Union
+
 import gym
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+from gym.utils import seeding
 from matplotlib import patches
+
 from alr_envs.classic_control.utils import check_self_collision
+from alr_envs.utils.mps.mp_environments import MPEnv
 
 
-class HoleReacher(gym.Env):
-
-    def __init__(self, n_links, hole_x, hole_width, hole_depth, allow_self_collision=False,
-                 allow_wall_collision=False, collision_penalty=1000):
+class HoleReacher(MPEnv):
 
+    def __init__(self, n_links, hole_x: Union[None, float] = None, hole_depth: Union[None, float] = None,
+                 hole_width: float = 1., random_start: bool = True, allow_self_collision: bool = False,
+                 allow_wall_collision: bool = False, collision_penalty: bool = 1000):
         self.n_links = n_links
         self.link_lengths = np.ones((n_links, 1))
 
-        # task
-        self.hole_x = hole_x  # x-position of center of hole
-        self.hole_width = hole_width  # width of hole
-        self.hole_depth = hole_depth  # depth of hole
+        self.random_start = random_start
 
-        self.bottom_center_of_hole = np.hstack([hole_x, -hole_depth])
-        self.top_center_of_hole = np.hstack([hole_x, 0])
-        self.left_wall_edge = np.hstack([hole_x - self.hole_width / 2, 0])
-        self.right_wall_edge = np.hstack([hole_x + self.hole_width / 2, 0])
+        # provided initial parameters
+        self._hole_x = hole_x  # x-position of center of hole
+        self._hole_width = hole_width  # width of hole
+        self._hole_depth = hole_depth  # depth of hole
+
+        # temp containers to store current setting
+        self._tmp_hole_x = None
+        self._tmp_hole_width = None
+        self._tmp_hole_depth = None
 
         # collision
         self.allow_self_collision = allow_self_collision
@@ -29,11 +36,11 @@ class HoleReacher(gym.Env):
         self.collision_penalty = collision_penalty
 
         # state
-        self._joints = None
         self._joint_angles = None
         self._angle_velocity = None
-        self.start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)])
-        self.start_vel = np.zeros(self.n_links)
+        self._joints = None
+        self._start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)])
+        self._start_vel = np.zeros(self.n_links)
 
         self.dt = 0.01
         # self.time_limit = 2
@@ -43,35 +50,64 @@ class HoleReacher(gym.Env):
             [np.pi] * self.n_links,  # cos
             [np.pi] * self.n_links,  # sin
             [np.inf] * self.n_links,  # velocity
+            [np.inf],  # hole width
+            [np.inf],  # hole depth
             [np.inf] * 2,  # x-y coordinates of target distance
             [np.inf]  # env steps, because reward start after n steps TODO: Maybe
         ])
         self.action_space = gym.spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape)
         self.observation_space = gym.spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
 
+        plt.ion()
         self.fig = None
-        rect_1 = patches.Rectangle((-self.n_links, -1),
-                                   self.n_links + self.hole_x - self.hole_width / 2, 1,
-                                   fill=True, edgecolor='k', facecolor='k')
-        rect_2 = patches.Rectangle((self.hole_x + self.hole_width / 2, -1),
-                                   self.n_links - self.hole_x + self.hole_width / 2, 1,
-                                   fill=True, edgecolor='k', facecolor='k')
-        rect_3 = patches.Rectangle((self.hole_x - self.hole_width / 2, -1), self.hole_width,
-                                   1 - self.hole_depth,
-                                   fill=True, edgecolor='k', facecolor='k')
 
-        self.patches = [rect_1, rect_2, rect_3]
+        self.seed()
+
+    @property
+    def corrected_obs_index(self):
+        return np.hstack([
+            [self.random_start] * self.n_links,  # cos
+            [self.random_start] * self.n_links,  # sin
+            [self.random_start] * self.n_links,  # velocity
+            [self._hole_width is None],  # hole width
+            [self._hole_depth is None],  # hole width
+            [True] * 2,  # x-y coordinates of target distance
+            [False]  # env steps
+        ])
+
+    def seed(self, seed=None):
+        self.np_random, seed = seeding.np_random(seed)
+        return [seed]
 
     @property
     def end_effector(self):
         return self._joints[self.n_links].T
 
-    def configure(self, context):
-        pass
+    def _generate_hole(self):
+        hole_x = self.np_random.uniform(0.5, 3.5, 1) if self._hole_x is None else np.copy(self._hole_x)
+        hole_width = self.np_random.uniform(0.5, 0.1, 1) if self._hole_width is None else np.copy(self._hole_width)
+        # TODO we do not want this right now.
+        hole_depth = self.np_random.uniform(1, 1, 1) if self._hole_depth is None else np.copy(self._hole_depth)
+
+        self.bottom_center_of_hole = np.hstack([hole_x, -hole_depth])
+        self.top_center_of_hole = np.hstack([hole_x, 0])
+        self.left_wall_edge = np.hstack([hole_x - hole_width / 2, 0])
+        self.right_wall_edge = np.hstack([hole_x + hole_width / 2, 0])
+
+        return hole_x, hole_width, hole_depth
 
     def reset(self):
-        self._joint_angles = self.start_pos
-        self._angle_velocity = self.start_vel
+        if self.random_start:
+            # MAybe change more than dirst seed
+            first_joint = self.np_random.uniform(np.pi / 4, 3 * np.pi / 4)
+            self._joint_angles = np.hstack([[first_joint], np.zeros(self.n_links - 1)])
+        else:
+            self._joint_angles = self._start_pos
+
+        self._tmp_hole_x, self._tmp_hole_width, self._tmp_hole_depth = self._generate_hole()
+        self.set_patches()
+
+        self._angle_velocity = self._start_vel
         self._joints = np.zeros((self.n_links + 1, 2))
         self._update_joints()
         self._steps = 0
@@ -96,15 +132,14 @@ class HoleReacher(gym.Env):
         success = False
         reward = 0
         if not self._is_collided:
+            # return reward only in last time step
             if self._steps == 199:
                 dist = np.linalg.norm(self.end_effector - self.bottom_center_of_hole)
                 reward = - dist ** 2
                 success = dist < 0.005
         else:
+            # Episode terminates when colliding, hence return reward
             dist = np.linalg.norm(self.end_effector - self.bottom_center_of_hole)
-            # if self.collision_penalty != 0:
-            #     reward = -self.collision_penalty
-            # else:
             reward = - dist ** 2 - self.collision_penalty
 
         reward -= 5e-8 * np.sum(acc ** 2)
@@ -112,8 +147,6 @@ class HoleReacher(gym.Env):
         info = {"is_collided": self._is_collided, "is_success": success}
 
         self._steps += 1
-
-        # done = self._steps * self.dt > self.time_limit or self._is_collided
         done = self._is_collided
 
         return self._get_obs().copy(), reward, done, info
@@ -148,6 +181,8 @@ class HoleReacher(gym.Env):
             np.cos(theta),
             np.sin(theta),
             self._angle_velocity,
+            self._hole_width,
+            self._hole_depth,
             self.end_effector - self.bottom_center_of_hole,
             self._steps
         ])
@@ -155,31 +190,26 @@ class HoleReacher(gym.Env):
     def get_forward_kinematics(self, num_points_per_link=1):
         theta = self._joint_angles[:, None]
 
-        if num_points_per_link > 1:
-            intermediate_points = np.linspace(0, 1, num_points_per_link)
-        else:
-            intermediate_points = 1
-
+        intermediate_points = np.linspace(0, 1, num_points_per_link) if num_points_per_link > 1 else 1
         accumulated_theta = np.cumsum(theta, axis=0)
-
-        endeffector = np.zeros(shape=(self.n_links, num_points_per_link, 2))
+        end_effector = np.zeros(shape=(self.n_links, num_points_per_link, 2))
 
         x = np.cos(accumulated_theta) * self.link_lengths * intermediate_points
         y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points
 
-        endeffector[0, :, 0] = x[0, :]
-        endeffector[0, :, 1] = y[0, :]
+        end_effector[0, :, 0] = x[0, :]
+        end_effector[0, :, 1] = y[0, :]
 
         for i in range(1, self.n_links):
-            endeffector[i, :, 0] = x[i, :] + endeffector[i - 1, -1, 0]
-            endeffector[i, :, 1] = y[i, :] + endeffector[i - 1, -1, 1]
+            end_effector[i, :, 0] = x[i, :] + end_effector[i - 1, -1, 0]
+            end_effector[i, :, 1] = y[i, :] + end_effector[i - 1, -1, 1]
 
-        return np.squeeze(endeffector + self._joints[0, :])
+        return np.squeeze(end_effector + self._joints[0, :])
 
     def check_wall_collision(self, line_points):
 
         # all points that are before the hole in x
-        r, c = np.where(line_points[:, :, 0] < (self.hole_x - self.hole_width / 2))
+        r, c = np.where(line_points[:, :, 0] < (self._tmp_hole_x - self._tmp_hole_width / 2))
 
         # check if any of those points are below surface
         nr_line_points_below_surface_before_hole = np.sum(line_points[r, c, 1] < 0)
@@ -188,7 +218,7 @@ class HoleReacher(gym.Env):
             return True
 
         # all points that are after the hole in x
-        r, c = np.where(line_points[:, :, 0] > (self.hole_x + self.hole_width / 2))
+        r, c = np.where(line_points[:, :, 0] > (self._tmp_hole_x + self._tmp_hole_width / 2))
 
         # check if any of those points are below surface
         nr_line_points_below_surface_after_hole = np.sum(line_points[r, c, 1] < 0)
@@ -197,11 +227,11 @@ class HoleReacher(gym.Env):
             return True
 
         # all points that are above the hole
-        r, c = np.where((line_points[:, :, 0] > (self.hole_x - self.hole_width / 2)) & (
-                line_points[:, :, 0] < (self.hole_x + self.hole_width / 2)))
+        r, c = np.where((line_points[:, :, 0] > (self._tmp_hole_x - self._tmp_hole_width / 2)) & (
+                line_points[:, :, 0] < (self._tmp_hole_x + self._tmp_hole_width / 2)))
 
         # check if any of those points are below surface
-        nr_line_points_below_surface_in_hole = np.sum(line_points[r, c, 1] < -self.hole_depth)
+        nr_line_points_below_surface_in_hole = np.sum(line_points[r, c, 1] < -self._tmp_hole_depth)
 
         if nr_line_points_below_surface_in_hole > 0:
             return True
@@ -210,28 +240,33 @@ class HoleReacher(gym.Env):
 
     def render(self, mode='human'):
         if self.fig is None:
+            plt.ion()
             self.fig = plt.figure()
-            # plt.ion()
-            # plt.pause(0.01)
-        else:
-            plt.figure(self.fig.number)
+            ax = self.fig.add_subplot(1, 1, 1)
+
+            # limits
+            lim = np.sum(self.link_lengths) + 0.5
+            ax.set_xlim([-lim, lim])
+            ax.set_ylim([-1.1, lim])
+
+            self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
+            self.set_patches()
+            self.fig.show()
 
         if mode == "human":
-            plt.cla()
-            plt.title(f"Iteration: {self._steps}, distance: {self.end_effector - self.bottom_center_of_hole}")
+            self.fig.gca().set_title(
+                f"Iteration: {self._steps}, distance: {self.end_effector - self.bottom_center_of_hole}")
 
             # Arm
             plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
 
-            # Add the patch to the Axes
-            [plt.gca().add_patch(rect) for rect in self.patches]
+            # Arm
+            self.line.set_xdata(self._joints[:, 0])
+            self.line.set_ydata(self._joints[:, 1])
 
-            lim = np.sum(self.link_lengths) + 0.5
-            plt.xlim([-lim, lim])
-            plt.ylim([-1.1, lim])
-            # plt.draw()
-            plt.pause(1e-4)  # pushes window to foreground, which is annoying.
-            # self.fig.canvas.flush_events()
+            self.fig.canvas.draw()
+            self.fig.canvas.flush_events()
+            # self.fig.show()
 
         elif mode == "partial":
             if self._steps == 1:
@@ -266,6 +301,24 @@ class HoleReacher(gym.Env):
 
                 plt.pause(0.01)
 
+    def set_patches(self):
+        if self.fig is not None:
+            self.fig.gca().patches = []
+            rect_1 = patches.Rectangle((-self.n_links, -1), self.n_links + self._tmp_hole_x - self._tmp_hole_width / 2,
+                                       1,
+                                       fill=True, edgecolor='k', facecolor='k')
+            rect_2 = patches.Rectangle((self._tmp_hole_x + self._tmp_hole_width / 2, -1),
+                                       self.n_links - self._tmp_hole_x + self._tmp_hole_width / 2, 1,
+                                       fill=True, edgecolor='k', facecolor='k')
+            rect_3 = patches.Rectangle((self._tmp_hole_x - self._tmp_hole_width / 2, -1), self._tmp_hole_width,
+                                       1 - self._tmp_hole_depth,
+                                       fill=True, edgecolor='k', facecolor='k')
+
+            # Add the patch to the Axes
+            self.fig.gca().add_patch(rect_1)
+            self.fig.gca().add_patch(rect_2)
+            self.fig.gca().add_patch(rect_3)
+
     def close(self):
         if self.fig is not None:
             plt.close(self.fig)
@@ -274,8 +327,8 @@ class HoleReacher(gym.Env):
 if __name__ == '__main__':
     nl = 5
     render_mode = "human"  # "human" or "partial" or "final"
-    env = HoleReacher(n_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=0.15,
-                      hole_depth=1, hole_x=1)
+    env = HoleReacher(n_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=None,
+                      hole_depth=1, hole_x=None)
     env.reset()
     # env.render(mode=render_mode)
 
@@ -285,11 +338,13 @@ if __name__ == '__main__':
         ac = 2 * env.action_space.sample()
         # ac[0] += np.pi/2
         obs, rew, d, info = env.step(ac)
-        env.render(mode=render_mode)
+        # if i % 1 == 0:
+        if i == 0:
+            env.render(mode=render_mode)
 
         print(rew)
 
         if d:
-            break
+            env.reset()
 
     env.close()
diff --git a/alr_envs/mujoco/ball_in_a_cup/utils.py b/alr_envs/mujoco/ball_in_a_cup/utils.py
index bfec3cf..d368e45 100644
--- a/alr_envs/mujoco/ball_in_a_cup/utils.py
+++ b/alr_envs/mujoco/ball_in_a_cup/utils.py
@@ -1,5 +1,5 @@
-from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper
-from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper
+from alr_envs.utils.mps.detpmp_wrapper import DetPMPWrapper
+from alr_envs.utils.mps.dmp_wrapper import DmpWrapper
 from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup import ALRBallInACupEnv
 
 
diff --git a/alr_envs/mujoco/beerpong/utils.py b/alr_envs/mujoco/beerpong/utils.py
index bfbc2a1..cdcbd13 100644
--- a/alr_envs/mujoco/beerpong/utils.py
+++ b/alr_envs/mujoco/beerpong/utils.py
@@ -1,4 +1,4 @@
-from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper
+from alr_envs.utils.mps.detpmp_wrapper import DetPMPWrapper
 from alr_envs.mujoco.beerpong.beerpong import ALRBeerpongEnv
 from alr_envs.mujoco.beerpong.beerpong_simple import ALRBeerpongEnv as ALRBeerpongEnvSimple
 
diff --git a/alr_envs/utils/legacy/utils.py b/alr_envs/utils/legacy/utils.py
index c158cae..fbcb34a 100644
--- a/alr_envs/utils/legacy/utils.py
+++ b/alr_envs/utils/legacy/utils.py
@@ -1,7 +1,7 @@
 import alr_envs.classic_control.hole_reacher as hr
 import alr_envs.classic_control.viapoint_reacher as vpr
-from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper
-from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper
+from alr_envs.utils.mps.dmp_wrapper import DmpWrapper
+from alr_envs.utils.mps.detpmp_wrapper import DetPMPWrapper
 import numpy as np
 
 
@@ -65,7 +65,7 @@ def make_holereacher_env(rank, seed=0):
                           dt=_env.dt,
                           learn_goal=True,
                           alpha_phase=2,
-                          start_pos=_env.start_pos,
+                          start_pos=_env._start_pos,
                           policy_type="velocity",
                           weights_scale=50,
                           goal_scale=0.1
@@ -105,7 +105,7 @@ def make_holereacher_fix_goal_env(rank, seed=0):
                           learn_goal=False,
                           final_pos=np.array([2.02669572, -1.25966385, -1.51618198, -0.80946476,  0.02012344]),
                           alpha_phase=2,
-                          start_pos=_env.start_pos,
+                          start_pos=_env._start_pos,
                           policy_type="velocity",
                           weights_scale=50,
                           goal_scale=1
@@ -142,7 +142,7 @@ def make_holereacher_env_pmp(rank, seed=0):
                              num_basis=5,
                              width=0.02,
                              policy_type="velocity",
-                             start_pos=_env.start_pos,
+                             start_pos=_env._start_pos,
                              duration=2,
                              post_traj_time=0,
                              dt=_env.dt,
diff --git a/alr_envs/utils/make_env_helpers.py b/alr_envs/utils/make_env_helpers.py
index c0e55b4..d455496 100644
--- a/alr_envs/utils/make_env_helpers.py
+++ b/alr_envs/utils/make_env_helpers.py
@@ -1,5 +1,5 @@
-from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper
-from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper
+from alr_envs.utils.mps.dmp_wrapper import DmpWrapper
+from alr_envs.utils.mps.detpmp_wrapper import DetPMPWrapper
 import gym
 from gym.vector.utils import write_to_shared_memory
 import sys
diff --git a/alr_envs/utils/wrapper/__init__.py b/alr_envs/utils/mps/__init__.py
similarity index 100%
rename from alr_envs/utils/wrapper/__init__.py
rename to alr_envs/utils/mps/__init__.py
diff --git a/alr_envs/utils/wrapper/detpmp_wrapper.py b/alr_envs/utils/mps/detpmp_wrapper.py
similarity index 66%
rename from alr_envs/utils/wrapper/detpmp_wrapper.py
rename to alr_envs/utils/mps/detpmp_wrapper.py
index 62b93d5..63de98b 100644
--- a/alr_envs/utils/wrapper/detpmp_wrapper.py
+++ b/alr_envs/utils/mps/detpmp_wrapper.py
@@ -2,17 +2,18 @@ import gym
 import numpy as np
 from mp_lib import det_promp
 
-from alr_envs.utils.wrapper.mp_wrapper import MPWrapper
+from alr_envs.utils.mps.mp_environments import MPEnv
+from alr_envs.utils.mps.mp_wrapper import MPWrapper
 
 
 class DetPMPWrapper(MPWrapper):
-    def __init__(self, env, num_dof, num_basis, width, start_pos=None, duration=1, dt=0.01, post_traj_time=0.,
-                 policy_type=None, weights_scale=1, zero_start=False, zero_goal=False, **mp_kwargs):
+    def __init__(self, env: MPEnv, num_dof: int, num_basis: int, width: int, start_pos=None, duration: int = 1,
+                 dt: float = 0.01, post_traj_time: float = 0., policy_type: str = None, weights_scale: float = 1.,
+                 zero_start: bool = False, zero_goal: bool = False, **mp_kwargs):
         # self.duration = duration  # seconds
 
-        super().__init__(env, num_dof, duration, dt, post_traj_time, policy_type, weights_scale,
-                         num_basis=num_basis, width=width, start_pos=start_pos, zero_start=zero_start,
-                         zero_goal=zero_goal)
+        super().__init__(env, num_dof, dt, duration, post_traj_time, policy_type, weights_scale, num_basis=num_basis,
+                         width=width, start_pos=start_pos, zero_start=zero_start, zero_goal=zero_goal, **mp_kwargs)
 
         action_bounds = np.inf * np.ones((self.mp.n_basis * self.mp.n_dof))
         self.action_space = gym.spaces.Box(low=-action_bounds, high=action_bounds, dtype=np.float32)
diff --git a/alr_envs/utils/wrapper/dmp_wrapper.py b/alr_envs/utils/mps/dmp_wrapper.py
similarity index 53%
rename from alr_envs/utils/wrapper/dmp_wrapper.py
rename to alr_envs/utils/mps/dmp_wrapper.py
index 2a198db..e42205e 100644
--- a/alr_envs/utils/wrapper/dmp_wrapper.py
+++ b/alr_envs/utils/mps/dmp_wrapper.py
@@ -1,19 +1,18 @@
-from mp_lib.phase import ExpDecayPhaseGenerator
-from mp_lib.basis import DMPBasisGenerator
-from mp_lib import dmps
-import numpy as np
 import gym
+import numpy as np
+from mp_lib import dmps
+from mp_lib.basis import DMPBasisGenerator
+from mp_lib.phase import ExpDecayPhaseGenerator
 
-from alr_envs.utils.wrapper.mp_wrapper import MPWrapper
+from alr_envs.utils.mps.mp_environments import MPEnv
+from alr_envs.utils.mps.mp_wrapper import MPWrapper
 
 
 class DmpWrapper(MPWrapper):
 
-    def __init__(self, env: gym.Env, num_dof: int, num_basis: int,
-                 # start_pos: np.ndarray = None,
-                 # final_pos: np.ndarray = None,
+    def __init__(self, env: MPEnv, num_dof: int, num_basis: int,
                  duration: int = 1, alpha_phase: float = 2., dt: float = None,
-                 learn_goal: bool = False, return_to_start: bool = False, post_traj_time: float = 0.,
+                 learn_goal: bool = False, post_traj_time: float = 0.,
                  weights_scale: float = 1., goal_scale: float = 1., bandwidth_factor: float = 3.,
                  policy_type: str = None, render_mode: str = None):
 
@@ -23,8 +22,6 @@ class DmpWrapper(MPWrapper):
             env:
             num_dof:
             num_basis:
-            start_pos:
-            final_pos:
             duration:
             alpha_phase:
             dt:
@@ -37,30 +34,17 @@ class DmpWrapper(MPWrapper):
         self.learn_goal = learn_goal
         dt = env.dt if hasattr(env, "dt") else dt
         assert dt is not None
-        # start_pos = start_pos if start_pos is not None else env.start_pos if hasattr(env, "start_pos") else None
-        # TODO: assert start_pos is not None  # start_pos will be set in initialize, do we need this here?
-        # if learn_goal:
-            # final_pos = np.zeros_like(start_pos)  # arbitrary, will be learned
-            # final_pos = np.zeros((1, num_dof))  # arbitrary, will be learned
-        # else:
-        #     final_pos = final_pos if final_pos is not None else start_pos if return_to_start else None
-        # assert final_pos is not None
         self.t = np.linspace(0, duration, int(duration / dt))
         self.goal_scale = goal_scale
 
-        super().__init__(env, num_dof, duration, dt, post_traj_time, policy_type, weights_scale, render_mode,
-                         num_basis=num_basis,
-                         # start_pos=start_pos, final_pos=final_pos,
-                         alpha_phase=alpha_phase,
-                         bandwidth_factor=bandwidth_factor)
+        super().__init__(env, num_dof, dt, duration, post_traj_time, policy_type, weights_scale, render_mode,
+                         num_basis=num_basis, alpha_phase=alpha_phase, bandwidth_factor=bandwidth_factor)
 
         action_bounds = np.inf * np.ones((np.prod(self.mp.dmp_weights.shape) + (num_dof if learn_goal else 0)))
         self.action_space = gym.spaces.Box(low=-action_bounds, high=action_bounds, dtype=np.float32)
 
-    def initialize_mp(self, num_dof: int, duration: int, dt: float, num_basis: int = 5,
-                      # start_pos: np.ndarray = None,
-                      # final_pos: np.ndarray = None,
-                      alpha_phase: float = 2., bandwidth_factor: float = 3.):
+    def initialize_mp(self, num_dof: int, duration: int, dt: float, num_basis: int = 5, alpha_phase: float = 2.,
+                      bandwidth_factor: int = 3):
 
         phase_generator = ExpDecayPhaseGenerator(alpha_phase=alpha_phase, duration=duration)
         basis_generator = DMPBasisGenerator(phase_generator, duration=duration, num_basis=num_basis,
@@ -69,15 +53,6 @@ class DmpWrapper(MPWrapper):
         dmp = dmps.DMP(num_dof=num_dof, basis_generator=basis_generator, phase_generator=phase_generator,
                        num_time_steps=int(duration / dt), dt=dt)
 
-        # dmp.dmp_start_pos = start_pos.reshape((1, num_dof))
-        # in a contextual environment, the start_pos may be not fixed, set in mp_rollout?
-        # TODO: Should we set start_pos in init at all? It's only used after calling rollout anyway...
-        # dmp.dmp_start_pos = start_pos.reshape((1, num_dof)) if start_pos is not None else np.zeros((1, num_dof))
-
-        # weights = np.zeros((num_basis, num_dof))
-        # goal_pos = np.zeros(num_dof) if self.learn_goal else final_pos
-
-        # dmp.set_weights(weights, goal_pos)
         return dmp
 
     def goal_and_weights(self, params):
@@ -87,18 +62,15 @@ class DmpWrapper(MPWrapper):
         if self.learn_goal:
             goal_pos = params[0, -self.mp.num_dimensions:]  # [num_dof]
             params = params[:, :-self.mp.num_dimensions]  # [1,num_dof]
-            # weight_matrix = np.reshape(params[:, :-self.num_dof], [self.num_basis, self.num_dof])
         else:
             goal_pos = self.env.goal_pos  # self.mp.dmp_goal_pos.flatten()
             assert goal_pos is not None
-            # weight_matrix = np.reshape(params, [self.num_basis, self.num_dof])
 
-        weight_matrix = np.reshape(params, self.mp.dmp_weights.shape)
+        weight_matrix = np.reshape(params, self.mp.dmp_weights.shape)  # [num_basis, num_dof]
         return goal_pos * self.goal_scale, weight_matrix * self.weights_scale
 
     def mp_rollout(self, action):
-        # if self.mp.start_pos is None:
-        self.mp.dmp_start_pos = self.env.init_qpos  # start_pos
+        self.mp.dmp_start_pos = self.env.start_pos
         goal_pos, weight_matrix = self.goal_and_weights(action)
         self.mp.set_weights(weight_matrix, goal_pos)
         return self.mp.reference_trajectory(self.t)
diff --git a/alr_envs/utils/mps/mp_environments.py b/alr_envs/utils/mps/mp_environments.py
new file mode 100644
index 0000000..f720f2f
--- /dev/null
+++ b/alr_envs/utils/mps/mp_environments.py
@@ -0,0 +1,33 @@
+from abc import abstractmethod
+from typing import Union
+
+import gym
+import numpy as np
+
+
+class MPEnv(gym.Env):
+
+    @property
+    @abstractmethod
+    def corrected_obs_index(self):
+        """Returns boolean value for each observation entry
+        whether the observation is returned by the DMP for the contextual case or not.
+        This effectively allows to filter unwanted or unnecessary observations from the full step-based case.
+        """
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def start_pos(self) -> Union[float, int, np.ndarray]:
+        """
+        Returns the current position of the joints
+        """
+        raise NotImplementedError()
+
+    @property
+    def goal_pos(self) -> Union[float, int, np.ndarray]:
+        """
+        Returns the current final position of the joints for the MP.
+        By default this returns the starting position.
+        """
+        return self.start_pos
diff --git a/alr_envs/utils/wrapper/mp_wrapper.py b/alr_envs/utils/mps/mp_wrapper.py
similarity index 68%
rename from alr_envs/utils/wrapper/mp_wrapper.py
rename to alr_envs/utils/mps/mp_wrapper.py
index adeba55..621de00 100644
--- a/alr_envs/utils/wrapper/mp_wrapper.py
+++ b/alr_envs/utils/mps/mp_wrapper.py
@@ -1,32 +1,18 @@
 from abc import ABC, abstractmethod
-from collections import defaultdict
 
 import gym
 import numpy as np
 
+from alr_envs.utils.mps.mp_environments import MPEnv
 from alr_envs.utils.policies import get_policy_class
 
 
 class MPWrapper(gym.Wrapper, ABC):
 
-    def __init__(self,
-                 env: gym.Env,
-                 num_dof: int,
-                 duration: int = 1,
-                 dt: float = None,
-                 post_traj_time: float = 0.,
-                 policy_type: str = None,
-                 weights_scale: float = 1.,
-                 render_mode: str = None,
-                 **mp_kwargs
-                 ):
+    def __init__(self, env: MPEnv, num_dof: int, dt: float, duration: int = 1, post_traj_time: float = 0.,
+                 policy_type: str = None, weights_scale: float = 1., render_mode: str = None, **mp_kwargs):
         super().__init__(env)
 
-        # self.num_dof = num_dof
-        # self.num_basis = num_basis
-        # self.duration = duration  # seconds
-
-        # dt = env.dt if hasattr(env, "dt") else dt
         assert dt is not None  # this should never happen as MPWrapper is a base class
         self.post_traj_steps = int(post_traj_time / dt)
 
@@ -40,8 +26,11 @@ class MPWrapper(gym.Wrapper, ABC):
         self.render_mode = render_mode
         self.render_kwargs = {}
 
-    # TODO: not yet final
+    # TODO: @Max I think this should not be in this class, this functionality should be part of your sampler.
     def __call__(self, params, contexts=None):
+        """
+        Can be used to provide a batch of parameter sets
+        """
         params = np.atleast_2d(params)
         obs = []
         rewards = []
@@ -63,7 +52,7 @@ class MPWrapper(gym.Wrapper, ABC):
 
     def reset(self):
         obs = self.env.reset()
-        return obs
+        return obs[self.env]
 
     def step(self, action: np.ndarray):
         """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step"""
@@ -77,15 +66,9 @@ class MPWrapper(gym.Wrapper, ABC):
         # self._velocity = velocity
 
         rewards = 0
-        # infos = defaultdict(list)
-
-        # TODO: @Max Why do we need this configure, states should be part of the model
-        # TODO: Ask Onur if the context distribution needs to be outside the environment
-        # TODO: For now create a new env with each context
-        # TODO: Explicitly call reset before step to obtain context from obs?
-        # self.env.configure(context)
-        # obs = self.env.reset()
         info = {}
+        # create random obs as the reset function is called externally
+        obs = self.env.observation_space.sample()
 
         for t, pos_vel in enumerate(zip(trajectory, velocity)):
             ac = self.policy.get_action(pos_vel[0], pos_vel[1])
@@ -107,18 +90,6 @@ class MPWrapper(gym.Wrapper, ABC):
         self.render_mode = mode
         self.render_kwargs = kwargs
 
-    # def __call__(self, actions):
-    #     return self.step(actions)
-        # params = np.atleast_2d(params)
-        # rewards = []
-        # infos = []
-        # for p, c in zip(params, contexts):
-        #     reward, info = self.rollout(p, c)
-        #     rewards.append(reward)
-        #     infos.append(info)
-        #
-        # return np.array(rewards), infos
-
     @abstractmethod
     def mp_rollout(self, action):
         """

From 6ae195962c4d0de08f901103caf7a5a807608055 Mon Sep 17 00:00:00 2001
From: ottofabian <fabi.otto@t-online.de>
Date: Wed, 12 May 2021 17:48:57 +0200
Subject: [PATCH 2/2] adjusted classic control environments to new interface

---
 alr_envs/__init__.py                          | 147 +++++-----
 alr_envs/classic_control/__init__.py          |   2 +-
 .../episodic_simple_reacher.py                |   2 +-
 alr_envs/classic_control/hole_reacher.py      | 259 ++++++++----------
 alr_envs/classic_control/simple_reacher.py    | 222 +++++++++------
 alr_envs/classic_control/viapoint_reacher.py  | 206 +++++++++-----
 alr_envs/mujoco/ball_in_a_cup/utils.py        |  46 +---
 alr_envs/mujoco/beerpong/utils.py             |  45 +--
 alr_envs/utils/legacy/utils.py                |  57 ++--
 alr_envs/utils/mps/detpmp_wrapper.py          |  14 +-
 alr_envs/utils/mps/dmp_wrapper.py             |   2 +-
 alr_envs/utils/mps/mp_environments.py         |   2 +-
 alr_envs/utils/mps/mp_wrapper.py              |  11 +-
 example.py                                    |   9 +-
 14 files changed, 535 insertions(+), 489 deletions(-)

diff --git a/alr_envs/__init__.py b/alr_envs/__init__.py
index fe32c0b..17d5541 100644
--- a/alr_envs/__init__.py
+++ b/alr_envs/__init__.py
@@ -1,6 +1,7 @@
 from gym.envs.registration import register
 
 from alr_envs.stochastic_search.functions.f_rosenbrock import Rosenbrock
+
 # from alr_envs.utils.mps.dmp_wrapper import DmpWrapper
 
 # Mujoco
@@ -71,6 +72,17 @@ register(
     }
 )
 
+## Balancing Reacher
+
+register(
+    id='Balancing-v0',
+    entry_point='alr_envs.mujoco:BalancingEnv',
+    max_episode_steps=200,
+    kwargs={
+        "n_links": 5,
+    }
+)
+
 register(
     id='ALRBallInACupSimple-v0',
     entry_point='alr_envs.mujoco:ALRBallInACupEnv',
@@ -101,15 +113,7 @@ register(
 
 # Classic control
 
-register(
-    id='Balancing-v0',
-    entry_point='alr_envs.mujoco:BalancingEnv',
-    max_episode_steps=200,
-    kwargs={
-        "n_links": 5,
-    }
-)
-
+## Simple Reacher
 register(
     id='SimpleReacher-v0',
     entry_point='alr_envs.classic_control:SimpleReacherEnv',
@@ -129,25 +133,6 @@ register(
     }
 )
 
-register(
-    id='EpisodicSimpleReacher-v0',
-    entry_point='alr_envs.classic_control:EpisodicSimpleReacherEnv',
-    max_episode_steps=200,
-    kwargs={
-        "n_links": 2,
-    }
-)
-
-register(
-    id='EpisodicSimpleReacher-v1',
-    entry_point='alr_envs.classic_control:EpisodicSimpleReacherEnv',
-    max_episode_steps=200,
-    kwargs={
-        "n_links": 2,
-        "random_start": False
-    }
-)
-
 register(
     id='LongSimpleReacher-v0',
     entry_point='alr_envs.classic_control:SimpleReacherEnv',
@@ -157,6 +142,18 @@ register(
     }
 )
 
+register(
+    id='LongSimpleReacher-v1',
+    entry_point='alr_envs.classic_control:SimpleReacherEnv',
+    max_episode_steps=200,
+    kwargs={
+        "n_links": 5,
+        "random_start": False
+    }
+)
+
+## Viapoint Reacher
+
 register(
     id='ViaPointReacher-v0',
     entry_point='alr_envs.classic_control.viapoint_reacher:ViaPointReacher',
@@ -168,27 +165,45 @@ register(
     }
 )
 
+## Hole Reacher
 register(
     id='HoleReacher-v0',
-    entry_point='alr_envs.classic_control.hole_reacher:HoleReacher',
+    entry_point='alr_envs.classic_control.hole_reacher:HoleReacherEnv',
     max_episode_steps=200,
     kwargs={
         "n_links": 5,
         "allow_self_collision": False,
         "allow_wall_collision": False,
-        "hole_width": 0.25,
+        "hole_width": None,
         "hole_depth": 1,
-        "hole_x": 2,
+        "hole_x": None,
+        "collision_penalty": 100,
+    }
+)
+
+register(
+    id='HoleReacher-v1',
+    entry_point='alr_envs.classic_control.hole_reacher:HoleReacherEnv',
+    max_episode_steps=200,
+    kwargs={
+        "n_links": 5,
+        "random_start": False,
+        "allow_self_collision": False,
+        "allow_wall_collision": False,
+        "hole_width": None,
+        "hole_depth": 1,
+        "hole_x": None,
         "collision_penalty": 100,
     }
 )
 
 register(
     id='HoleReacher-v2',
-    entry_point='alr_envs.classic_control.hole_reacher_v2:HoleReacher',
+    entry_point='alr_envs.classic_control.hole_reacher:HoleReacherEnv',
     max_episode_steps=200,
     kwargs={
         "n_links": 5,
+        "random_start": False,
         "allow_self_collision": False,
         "allow_wall_collision": False,
         "hole_width": 0.25,
@@ -199,38 +214,24 @@ register(
 )
 
 # MP environments
-
-register(
-    id='SimpleReacherDMP-v0',
-    entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
-    # max_episode_steps=1,
-    kwargs={
-        "name": "alr_envs:EpisodicSimpleReacher-v0",
-        "num_dof": 2,
-        "num_basis": 5,
-        "duration": 2,
-        "alpha_phase": 2,
-        "learn_goal": True,
-        "policy_type": "velocity",
-        "weights_scale": 50,
-    }
-)
-
-register(
-    id='SimpleReacherDMP-v1',
-    entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
-    # max_episode_steps=1,
-    kwargs={
-        "name": "alr_envs:EpisodicSimpleReacher-v1",
-        "num_dof": 2,
-        "num_basis": 5,
-        "duration": 2,
-        "alpha_phase": 2,
-        "learn_goal": True,
-        "policy_type": "velocity",
-        "weights_scale": 50,
-    }
-)
+reacher_envs = ["SimpleReacher-v0", "SimpleReacher-v1", "LongSimpleReacher-v0", "LongSimpleReacher-v1"]
+for env in reacher_envs:
+    name = env.split("-")
+    register(
+        id=f'{name[0]}DMP-{name[1]}',
+        entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
+        # max_episode_steps=1,
+        kwargs={
+            "name": f"alr_envs:{env}",
+            "num_dof": 2 if "long" not in env.lower() else 5 ,
+            "num_basis": 5,
+            "duration": 2,
+            "alpha_phase": 2,
+            "learn_goal": True,
+            "policy_type": "velocity",
+            "weights_scale": 50,
+        }
+    )
 
 register(
     id='ViaPointReacherDMP-v0',
@@ -266,6 +267,24 @@ register(
     }
 )
 
+register(
+    id='HoleReacherDMP-v1',
+    entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
+    # max_episode_steps=1,
+    kwargs={
+        "name": "alr_envs:HoleReacher-v1",
+        "num_dof": 5,
+        "num_basis": 5,
+        "duration": 2,
+        "learn_goal": True,
+        "alpha_phase": 2,
+        "bandwidth_factor": 2,
+        "policy_type": "velocity",
+        "weights_scale": 50,
+        "goal_scale": 0.1
+    }
+)
+
 register(
     id='HoleReacherDMP-v2',
     entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
diff --git a/alr_envs/classic_control/__init__.py b/alr_envs/classic_control/__init__.py
index 8087136..fde30fc 100644
--- a/alr_envs/classic_control/__init__.py
+++ b/alr_envs/classic_control/__init__.py
@@ -1,4 +1,4 @@
 from alr_envs.classic_control.simple_reacher import SimpleReacherEnv
 from alr_envs.classic_control.episodic_simple_reacher import EpisodicSimpleReacherEnv
 from alr_envs.classic_control.viapoint_reacher import ViaPointReacher
-from alr_envs.classic_control.hole_reacher import HoleReacher
+from alr_envs.classic_control.hole_reacher import HoleReacherEnv
diff --git a/alr_envs/classic_control/episodic_simple_reacher.py b/alr_envs/classic_control/episodic_simple_reacher.py
index b02efe8..2e7f604 100644
--- a/alr_envs/classic_control/episodic_simple_reacher.py
+++ b/alr_envs/classic_control/episodic_simple_reacher.py
@@ -35,7 +35,7 @@ class EpisodicSimpleReacherEnv(SimpleReacherEnv):
 
     def _get_obs(self):
         if self.random_start:
-            theta = self._joint_angle
+            theta = self._joint_angles
             return np.hstack([
                 np.cos(theta),
                 np.sin(theta),
diff --git a/alr_envs/classic_control/hole_reacher.py b/alr_envs/classic_control/hole_reacher.py
index 45bc1c3..0e008aa 100644
--- a/alr_envs/classic_control/hole_reacher.py
+++ b/alr_envs/classic_control/hole_reacher.py
@@ -10,11 +10,12 @@ from alr_envs.classic_control.utils import check_self_collision
 from alr_envs.utils.mps.mp_environments import MPEnv
 
 
-class HoleReacher(MPEnv):
+class HoleReacherEnv(MPEnv):
 
-    def __init__(self, n_links, hole_x: Union[None, float] = None, hole_depth: Union[None, float] = None,
-                 hole_width: float = 1., random_start: bool = True, allow_self_collision: bool = False,
+    def __init__(self, n_links: int, hole_x: Union[None, float] = None, hole_depth: Union[None, float] = None,
+                 hole_width: float = 1., random_start: bool = False, allow_self_collision: bool = False,
                  allow_wall_collision: bool = False, collision_penalty: bool = 1000):
+
         self.n_links = n_links
         self.link_lengths = np.ones((n_links, 1))
 
@@ -25,10 +26,11 @@ class HoleReacher(MPEnv):
         self._hole_width = hole_width  # width of hole
         self._hole_depth = hole_depth  # depth of hole
 
-        # temp containers to store current setting
+        # temp container for current env state
         self._tmp_hole_x = None
         self._tmp_hole_width = None
         self._tmp_hole_depth = None
+        self._goal = None  # x-y coordinates for reaching the center at the bottom of the hole
 
         # collision
         self.allow_self_collision = allow_self_collision
@@ -36,14 +38,13 @@ class HoleReacher(MPEnv):
         self.collision_penalty = collision_penalty
 
         # state
+        self._joints = None
         self._joint_angles = None
         self._angle_velocity = None
-        self._joints = None
         self._start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)])
         self._start_vel = np.zeros(self.n_links)
 
         self.dt = 0.01
-        # self.time_limit = 2
 
         action_bound = np.pi * np.ones((self.n_links,))
         state_bound = np.hstack([
@@ -58,54 +59,43 @@ class HoleReacher(MPEnv):
         self.action_space = gym.spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape)
         self.observation_space = gym.spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
 
-        plt.ion()
+        # containers for plotting
+        self.metadata = {'render.modes': ["human", "partial"]}
         self.fig = None
 
+        self._steps = 0
         self.seed()
 
-    @property
-    def corrected_obs_index(self):
-        return np.hstack([
-            [self.random_start] * self.n_links,  # cos
-            [self.random_start] * self.n_links,  # sin
-            [self.random_start] * self.n_links,  # velocity
-            [self._hole_width is None],  # hole width
-            [self._hole_depth is None],  # hole width
-            [True] * 2,  # x-y coordinates of target distance
-            [False]  # env steps
-        ])
+    def step(self, action: np.ndarray):
+        """
+        A single step with an action in joint velocity space
+        """
 
-    def seed(self, seed=None):
-        self.np_random, seed = seeding.np_random(seed)
-        return [seed]
+        self._angle_velocity = action
+        self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
+        self._update_joints()
 
-    @property
-    def end_effector(self):
-        return self._joints[self.n_links].T
+        acc = (action - self._angle_velocity) / self.dt
+        reward, info = self._get_reward(acc)
 
-    def _generate_hole(self):
-        hole_x = self.np_random.uniform(0.5, 3.5, 1) if self._hole_x is None else np.copy(self._hole_x)
-        hole_width = self.np_random.uniform(0.5, 0.1, 1) if self._hole_width is None else np.copy(self._hole_width)
-        # TODO we do not want this right now.
-        hole_depth = self.np_random.uniform(1, 1, 1) if self._hole_depth is None else np.copy(self._hole_depth)
+        info.update({"is_collided": self._is_collided})
 
-        self.bottom_center_of_hole = np.hstack([hole_x, -hole_depth])
-        self.top_center_of_hole = np.hstack([hole_x, 0])
-        self.left_wall_edge = np.hstack([hole_x - hole_width / 2, 0])
-        self.right_wall_edge = np.hstack([hole_x + hole_width / 2, 0])
+        self._steps += 1
+        done = self._is_collided
 
-        return hole_x, hole_width, hole_depth
+        return self._get_obs().copy(), reward, done, info
 
     def reset(self):
         if self.random_start:
-            # MAybe change more than dirst seed
+            # Maybe change more than dirst seed
             first_joint = self.np_random.uniform(np.pi / 4, 3 * np.pi / 4)
             self._joint_angles = np.hstack([[first_joint], np.zeros(self.n_links - 1)])
+            self._start_pos = self._joint_angles.copy()
         else:
             self._joint_angles = self._start_pos
 
-        self._tmp_hole_x, self._tmp_hole_width, self._tmp_hole_depth = self._generate_hole()
-        self.set_patches()
+        self._generate_hole()
+        self._set_patches()
 
         self._angle_velocity = self._start_vel
         self._joints = np.zeros((self.n_links + 1, 2))
@@ -114,42 +104,14 @@ class HoleReacher(MPEnv):
 
         return self._get_obs().copy()
 
-    def step(self, action: np.ndarray):
-        """
-        a single step with an action in joint velocity space
-        """
-        vel = action  # + 0.01 * np.random.randn(self.num_links)
-        acc = (vel - self._angle_velocity) / self.dt
-        self._angle_velocity = vel
-        self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
-
-        self._update_joints()
-
-        # rew = self._reward()
-
-        # compute reward directly in step function
-
-        success = False
-        reward = 0
-        if not self._is_collided:
-            # return reward only in last time step
-            if self._steps == 199:
-                dist = np.linalg.norm(self.end_effector - self.bottom_center_of_hole)
-                reward = - dist ** 2
-                success = dist < 0.005
-        else:
-            # Episode terminates when colliding, hence return reward
-            dist = np.linalg.norm(self.end_effector - self.bottom_center_of_hole)
-            reward = - dist ** 2 - self.collision_penalty
-
-        reward -= 5e-8 * np.sum(acc ** 2)
-
-        info = {"is_collided": self._is_collided, "is_success": success}
-
-        self._steps += 1
-        done = self._is_collided
-
-        return self._get_obs().copy(), reward, done, info
+    def _generate_hole(self):
+        self._tmp_hole_x = self.np_random.uniform(0.5, 3.5, 1) if self._hole_x is None else np.copy(self._hole_x)
+        self._tmp_hole_width = self.np_random.uniform(0.5, 0.1, 1) if self._hole_width is None else np.copy(
+            self._hole_width)
+        # TODO we do not want this right now.
+        self._tmp_hole_depth = self.np_random.uniform(1, 1, 1) if self._hole_depth is None else np.copy(
+            self._hole_depth)
+        self._goal = np.hstack([self._tmp_hole_x, -self._tmp_hole_depth])
 
     def _update_joints(self):
         """
@@ -157,7 +119,7 @@ class HoleReacher(MPEnv):
         Returns:
 
         """
-        line_points_in_taskspace = self.get_forward_kinematics(num_points_per_link=20)
+        line_points_in_taskspace = self._get_forward_kinematics(num_points_per_link=20)
 
         self._joints[1:, 0] = self._joints[0, 0] + line_points_in_taskspace[:, -1, 0]
         self._joints[1:, 1] = self._joints[0, 1] + line_points_in_taskspace[:, -1, 1]
@@ -171,23 +133,43 @@ class HoleReacher(MPEnv):
                 self_collision = True
 
         if not self.allow_wall_collision:
-            wall_collision = self.check_wall_collision(line_points_in_taskspace)
+            wall_collision = self._check_wall_collision(line_points_in_taskspace)
 
         self._is_collided = self_collision or wall_collision
 
+    def _get_reward(self, acc: np.ndarray):
+        success = False
+        reward = -np.inf
+        if not self._is_collided:
+            dist = 0
+            # return reward only in last time step
+            if self._steps == 199:
+                dist = np.linalg.norm(self.end_effector - self._goal)
+                success = dist < 0.005
+        else:
+            # Episode terminates when colliding, hence return reward
+            dist = np.linalg.norm(self.end_effector - self._goal)
+            reward = -self.collision_penalty
+
+        reward -= dist ** 2
+        reward -= 5e-8 * np.sum(acc ** 2)
+        info = {"is_success": success}
+
+        return reward, info
+
     def _get_obs(self):
         theta = self._joint_angles
         return np.hstack([
             np.cos(theta),
             np.sin(theta),
             self._angle_velocity,
-            self._hole_width,
-            self._hole_depth,
-            self.end_effector - self.bottom_center_of_hole,
+            self._tmp_hole_width,
+            self._tmp_hole_depth,
+            self.end_effector - self._goal,
             self._steps
         ])
 
-    def get_forward_kinematics(self, num_points_per_link=1):
+    def _get_forward_kinematics(self, num_points_per_link=1):
         theta = self._joint_angles[:, None]
 
         intermediate_points = np.linspace(0, 1, num_points_per_link) if num_points_per_link > 1 else 1
@@ -206,7 +188,7 @@ class HoleReacher(MPEnv):
 
         return np.squeeze(end_effector + self._joints[0, :])
 
-    def check_wall_collision(self, line_points):
+    def _check_wall_collision(self, line_points):
 
         # all points that are before the hole in x
         r, c = np.where(line_points[:, :, 0] < (self._tmp_hole_x - self._tmp_hole_width / 2))
@@ -240,6 +222,7 @@ class HoleReacher(MPEnv):
 
     def render(self, mode='human'):
         if self.fig is None:
+            # Create base figure once on the beginning. Afterwards only update
             plt.ion()
             self.fig = plt.figure()
             ax = self.fig.add_subplot(1, 1, 1)
@@ -250,74 +233,74 @@ class HoleReacher(MPEnv):
             ax.set_ylim([-1.1, lim])
 
             self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
-            self.set_patches()
+            self._set_patches()
             self.fig.show()
 
+        self.fig.gca().set_title(
+            f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}")
+
         if mode == "human":
-            self.fig.gca().set_title(
-                f"Iteration: {self._steps}, distance: {self.end_effector - self.bottom_center_of_hole}")
 
-            # Arm
-            plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
-
-            # Arm
-            self.line.set_xdata(self._joints[:, 0])
-            self.line.set_ydata(self._joints[:, 1])
+            # arm
+            self.line.set_data(self._joints[:, 0], self._joints[:, 1])
 
             self.fig.canvas.draw()
             self.fig.canvas.flush_events()
-            # self.fig.show()
 
         elif mode == "partial":
-            if self._steps == 1:
-                # fig, ax = plt.subplots()
-                # Add the patch to the Axes
-                [plt.gca().add_patch(rect) for rect in self.patches]
-                # plt.pause(0.01)
-
             if self._steps % 20 == 0 or self._steps in [1, 199] or self._is_collided:
                 # Arm
-                plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k', alpha=self._steps / 200)
-                # ax.plot(line_points_in_taskspace[:, 0, 0],
-                #         line_points_in_taskspace[:, 0, 1],
-                #         line_points_in_taskspace[:, -1, 0],
-                #         line_points_in_taskspace[:, -1, 1], marker='o', color='k', alpha=t / 200)
+                plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k',
+                         alpha=self._steps / 200)
 
-                lim = np.sum(self.link_lengths) + 0.5
-                plt.xlim([-lim, lim])
-                plt.ylim([-1.1, lim])
-                plt.pause(0.01)
-
-        elif mode == "final":
-            if self._steps == 199 or self._is_collided:
-                # fig, ax = plt.subplots()
-
-                # Add the patch to the Axes
-                [plt.gca().add_patch(rect) for rect in self.patches]
-
-                plt.xlim(-self.n_links, self.n_links), plt.ylim(-1, self.n_links)
-                # Arm
-                plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
-
-                plt.pause(0.01)
-
-    def set_patches(self):
+    def _set_patches(self):
         if self.fig is not None:
             self.fig.gca().patches = []
-            rect_1 = patches.Rectangle((-self.n_links, -1), self.n_links + self._tmp_hole_x - self._tmp_hole_width / 2,
-                                       1,
-                                       fill=True, edgecolor='k', facecolor='k')
-            rect_2 = patches.Rectangle((self._tmp_hole_x + self._tmp_hole_width / 2, -1),
-                                       self.n_links - self._tmp_hole_x + self._tmp_hole_width / 2, 1,
-                                       fill=True, edgecolor='k', facecolor='k')
-            rect_3 = patches.Rectangle((self._tmp_hole_x - self._tmp_hole_width / 2, -1), self._tmp_hole_width,
-                                       1 - self._tmp_hole_depth,
-                                       fill=True, edgecolor='k', facecolor='k')
+            left_block = patches.Rectangle((-self.n_links, -self._tmp_hole_depth),
+                                           self.n_links + self._tmp_hole_x - self._tmp_hole_width / 2,
+                                           self._tmp_hole_depth,
+                                           fill=True, edgecolor='k', facecolor='k')
+            right_block = patches.Rectangle((self._tmp_hole_x + self._tmp_hole_width / 2, -self._tmp_hole_depth),
+                                            self.n_links - self._tmp_hole_x + self._tmp_hole_width / 2,
+                                            self._tmp_hole_depth,
+                                            fill=True, edgecolor='k', facecolor='k')
+            hole_floor = patches.Rectangle((self._tmp_hole_x - self._tmp_hole_width / 2, -self._tmp_hole_depth),
+                                           self._tmp_hole_width,
+                                           1 - self._tmp_hole_depth,
+                                           fill=True, edgecolor='k', facecolor='k')
 
             # Add the patch to the Axes
-            self.fig.gca().add_patch(rect_1)
-            self.fig.gca().add_patch(rect_2)
-            self.fig.gca().add_patch(rect_3)
+            self.fig.gca().add_patch(left_block)
+            self.fig.gca().add_patch(right_block)
+            self.fig.gca().add_patch(hole_floor)
+
+    @property
+    def active_obs(self):
+        return np.hstack([
+            [self.random_start] * self.n_links,  # cos
+            [self.random_start] * self.n_links,  # sin
+            [self.random_start] * self.n_links,  # velocity
+            [self._hole_width is None],  # hole width
+            [self._hole_depth is None],  # hole width
+            [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 seed(self, seed=None):
+        self.np_random, seed = seeding.np_random(seed)
+        return [seed]
+
+    @property
+    def end_effector(self):
+        return self._joints[self.n_links].T
 
     def close(self):
         if self.fig is not None:
@@ -327,20 +310,16 @@ class HoleReacher(MPEnv):
 if __name__ == '__main__':
     nl = 5
     render_mode = "human"  # "human" or "partial" or "final"
-    env = HoleReacher(n_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=None,
-                      hole_depth=1, hole_x=None)
-    env.reset()
-    # env.render(mode=render_mode)
+    env = HoleReacherEnv(n_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=None,
+                         hole_depth=1, hole_x=None)
+    obs = env.reset()
 
     for i in range(200):
         # objective.load_result("/tmp/cma")
         # test with random actions
         ac = 2 * env.action_space.sample()
-        # ac[0] += np.pi/2
         obs, rew, d, info = env.step(ac)
-        # if i % 1 == 0:
-        if i == 0:
-            env.render(mode=render_mode)
+        env.render(mode=render_mode)
 
         print(rew)
 
diff --git a/alr_envs/classic_control/simple_reacher.py b/alr_envs/classic_control/simple_reacher.py
index 7ca4ead..4e99ff1 100644
--- a/alr_envs/classic_control/simple_reacher.py
+++ b/alr_envs/classic_control/simple_reacher.py
@@ -1,24 +1,21 @@
-import gym
+from typing import Iterable, Union
+
 import matplotlib.pyplot as plt
 import numpy as np
 from gym import spaces
 from gym.utils import seeding
 
-from alr_envs.utils.utils import angle_normalize
+from alr_envs.utils.mps.mp_environments import MPEnv
 
 
-# if os.environ.get("DISPLAY", None):
-#     mpl.use('Qt5Agg')
-
-
-class SimpleReacherEnv(gym.Env):
+class SimpleReacherEnv(MPEnv):
     """
     Simple Reaching Task without any physics simulation.
     Returns no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions
     towards the end of the trajectory.
     """
 
-    def __init__(self, n_links, random_start=True):
+    def __init__(self, n_links: int, target: Union[None, Iterable] = None, random_start: bool = True):
         super().__init__()
         self.link_lengths = np.ones(n_links)
         self.n_links = n_links
@@ -26,17 +23,19 @@ class SimpleReacherEnv(gym.Env):
 
         self.random_start = random_start
 
-        self._goal = None
-
         self._joints = None
-        self._joint_angle = None
+        self._joint_angles = None
         self._angle_velocity = None
-        self._start_pos = None
+        self._start_pos = np.zeros(self.n_links)
+        self._start_vel = np.zeros(self.n_links)
 
-        self.max_torque = 1  # 10
+        self._target = target  # provided target value
+        self._goal = None  # updated goal value, does not change when target != None
+
+        self.max_torque = 1
         self.steps_before_reward = 199
 
-        action_bound = np.ones((self.n_links,))
+        action_bound = np.ones((self.n_links,)) * self.max_torque
         state_bound = np.hstack([
             [np.pi] * self.n_links,  # cos
             [np.pi] * self.n_links,  # sin
@@ -47,49 +46,50 @@ class SimpleReacherEnv(gym.Env):
         self.action_space = spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape)
         self.observation_space = spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
 
-        self.fig = None
+        # containers for plotting
         self.metadata = {'render.modes': ["human"]}
+        self.fig = None
 
         self._steps = 0
         self.seed()
 
     def step(self, action: np.ndarray):
+        """
+        A single step with action in torque space
+        """
 
         # action = self._add_action_noise(action)
-        action = np.clip(action, -self.max_torque, self.max_torque)
+        ac = np.clip(action, -self.max_torque, self.max_torque)
 
-        self._angle_velocity = self._angle_velocity + self.dt * action
-        self._joint_angle = angle_normalize(self._joint_angle + self.dt * self._angle_velocity)
+        self._angle_velocity = self._angle_velocity + self.dt * ac
+        self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
         self._update_joints()
-        self._steps += 1
 
         reward, info = self._get_reward(action)
 
-        # done = np.abs(self.end_effector - self._goal_pos) < 0.1
+        self._steps += 1
         done = False
 
         return self._get_obs().copy(), reward, done, info
 
-    def _add_action_noise(self, action: np.ndarray):
-        """
-        add unobserved Gaussian Noise N(0,0.01) to the actions
-        Args:
-            action:
+    def reset(self):
 
-        Returns: actions with noise
+        # TODO: maybe do initialisation more random?
+        # Sample only orientation of first link, i.e. the arm is always straight.
+        if self.random_start:
+            self._joint_angles = np.hstack([[self.np_random.uniform(-np.pi, np.pi)], np.zeros(self.n_links - 1)])
+            self._start_pos = self._joint_angles.copy()
+        else:
+            self._joint_angles = self._start_pos
 
-        """
-        return self.np_random.normal(0, 0.1, *action.shape) + action
+        self._generate_goal()
 
-    def _get_obs(self):
-        theta = self._joint_angle
-        return np.hstack([
-            np.cos(theta),
-            np.sin(theta),
-            self._angle_velocity,
-            self.end_effector - self._goal,
-            self._steps
-        ])
+        self._angle_velocity = self._start_vel
+        self._joints = np.zeros((self.n_links + 1, 2))
+        self._update_joints()
+        self._steps = 0
+
+        return self._get_obs().copy()
 
     def _update_joints(self):
         """
@@ -97,7 +97,7 @@ class SimpleReacherEnv(gym.Env):
         Returns:
 
         """
-        angles = np.cumsum(self._joint_angle)
+        angles = np.cumsum(self._joint_angles)
         x = self.link_lengths * np.vstack([np.cos(angles), np.sin(angles)])
         self._joints[1:] = self._joints[0] + np.cumsum(x.T, axis=0)
 
@@ -105,7 +105,6 @@ class SimpleReacherEnv(gym.Env):
         diff = self.end_effector - self._goal
         reward_dist = 0
 
-        # TODO: Is this the best option
         if self._steps >= self.steps_before_reward:
             reward_dist -= np.linalg.norm(diff)
             # reward_dist = np.exp(-0.1 * diff ** 2).mean()
@@ -115,67 +114,118 @@ class SimpleReacherEnv(gym.Env):
         reward = reward_dist - reward_ctrl
         return reward, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
 
-    def reset(self):
+    def _get_obs(self):
+        theta = self._joint_angles
+        return np.hstack([
+            np.cos(theta),
+            np.sin(theta),
+            self._angle_velocity,
+            self.end_effector - self._goal,
+            self._steps
+        ])
 
-        # TODO: maybe do initialisation more random?
-        # Sample only orientation of first link, i.e. the arm is always straight.
-        if self.random_start:
-            self._joint_angle = np.hstack([[self.np_random.uniform(-np.pi, np.pi)], np.zeros(self.n_links - 1)])
+    def _generate_goal(self):
+
+        if self._target is None:
+            # center = self._joints[0]
+            # # Sample uniformly in circle with radius R around center of reacher.
+            # R = np.sum(self.link_lengths)
+            # r = R * np.sqrt(self.np_random.uniform())
+            # theta = self.np_random.uniform() * 2 * np.pi
+            # goal = center + r * np.stack([np.cos(theta), np.sin(theta)])
+
+            total_length = np.sum(self.link_lengths)
+            goal = np.array([total_length, total_length])
+            while np.linalg.norm(goal) >= total_length:
+                goal = self.np_random.uniform(low=-total_length, high=total_length, size=2)
         else:
-            self._joint_angle = np.zeros(self.n_links)
+            goal = np.copy(self._target)
 
-        self._start_pos = self._joint_angle
-        self._angle_velocity = np.zeros(self.n_links)
-        self._joints = np.zeros((self.n_links + 1, 2))
-        self._update_joints()
-        self._steps = 0
+        self._goal = goal
 
-        self._goal = self._get_random_goal()
-        return self._get_obs().copy()
+    def render(self, mode='human'):  # pragma: no cover
+        if self.fig is None:
+            # Create base figure once on the beginning. Afterwards only update
+            plt.ion()
+            self.fig = plt.figure()
+            ax = self.fig.add_subplot(1, 1, 1)
 
-    def _get_random_goal(self):
-        center = self._joints[0]
+            # limits
+            lim = np.sum(self.link_lengths) + 0.5
+            ax.set_xlim([-lim, lim])
+            ax.set_ylim([-lim, lim])
 
-        # Sample uniformly in circle with radius R around center of reacher.
-        R = np.sum(self.link_lengths)
-        r = R * np.sqrt(self.np_random.uniform())
-        theta = self.np_random.uniform() * 2 * np.pi
-        return center + r * np.stack([np.cos(theta), np.sin(theta)])
+            self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
+            goal_pos = self._goal.T
+            self.goal_point, = ax.plot(goal_pos[0], goal_pos[1], 'gx')
+            self.goal_dist, = ax.plot([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]], 'g--')
+
+            self.fig.show()
+
+        self.fig.gca().set_title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}")
+
+        # goal
+        goal_pos = self._goal.T
+        if self._steps == 1:
+            self.goal_point.set_data(goal_pos[0], goal_pos[1])
+
+        # arm
+        self.line.set_data(self._joints[:, 0], self._joints[:, 1])
+
+        # distance between end effector and goal
+        self.goal_dist.set_data([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]])
+
+        self.fig.canvas.draw()
+        self.fig.canvas.flush_events()
+
+    @property
+    def active_obs(self):
+        return np.hstack([
+            [self.random_start] * self.n_links,  # cos
+            [self.random_start] * self.n_links,  # sin
+            [self.random_start] * self.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.")
 
     def seed(self, seed=None):
         self.np_random, seed = seeding.np_random(seed)
         return [seed]
 
-    def render(self, mode='human'):  # pragma: no cover
-        if self.fig is None:
-            self.fig = plt.figure()
-            plt.ion()
-            plt.show()
-        else:
-            plt.figure(self.fig.number)
-
-        plt.cla()
-        plt.title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}")
-
-        # Arm
-        plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
-
-        # goal
-        goal_pos = self._goal.T
-        plt.plot(goal_pos[0], goal_pos[1], 'gx')
-        # distance between end effector and goal
-        plt.plot([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]], 'g--')
-
-        lim = np.sum(self.link_lengths) + 0.5
-        plt.xlim([-lim, lim])
-        plt.ylim([-lim, lim])
-        # plt.draw()
-        # plt.pause(1e-4) pushes window to foreground, which is annoying.
-        self.fig.canvas.flush_events()
-
     def close(self):
         del self.fig
 
     @property
     def end_effector(self):
         return self._joints[self.n_links].T
+
+
+if __name__ == '__main__':
+    nl = 5
+    render_mode = "human"  # "human" or "partial" or "final"
+    env = SimpleReacherEnv(n_links=nl)
+    obs = env.reset()
+    print("First", obs)
+
+    for i in range(2000):
+        # objective.load_result("/tmp/cma")
+        # test with random actions
+        ac = 2 * env.action_space.sample()
+        # ac = np.ones(env.action_space.shape)
+        obs, rew, d, info = env.step(ac)
+        env.render(mode=render_mode)
+
+        print(obs[env.active_obs].shape)
+
+        if d or i % 200 == 0:
+            env.reset()
+
+    env.close()
diff --git a/alr_envs/classic_control/viapoint_reacher.py b/alr_envs/classic_control/viapoint_reacher.py
index 127bf77..ac36360 100644
--- a/alr_envs/classic_control/viapoint_reacher.py
+++ b/alr_envs/classic_control/viapoint_reacher.py
@@ -1,19 +1,31 @@
+from typing import Iterable, Union
+
 import gym
 import matplotlib.pyplot as plt
 import numpy as np
+from gym.utils import seeding
 
 from alr_envs.classic_control.utils import check_self_collision
+from alr_envs.utils.mps.mp_environments import MPEnv
 
 
-class ViaPointReacher(gym.Env):
+class ViaPointReacher(MPEnv):
 
-    def __init__(self, n_links, allow_self_collision=False, collision_penalty=1000):
-        self.num_links = n_links
+    def __init__(self, n_links, random_start: bool = True, via_target: Union[None, Iterable] = None,
+                 target: Union[None, Iterable] = None, allow_self_collision=False, collision_penalty=1000):
+
+        self.n_links = n_links
         self.link_lengths = np.ones((n_links, 1))
 
-        # task
-        self.via_point = np.ones(2)
-        self.goal_point = np.array((n_links, 0))
+        self.random_start = random_start
+
+        # provided initial parameters
+        self._target = target  # provided target value
+        self._via_target = via_target  # provided via point target value
+
+        # temp container for current env state
+        self._via_point = np.ones(2)
+        self._goal = np.array((n_links, 0))
 
         # collision
         self.allow_self_collision = allow_self_collision
@@ -23,78 +35,74 @@ class ViaPointReacher(gym.Env):
         self._joints = None
         self._joint_angles = None
         self._angle_velocity = None
-        self.start_pos = np.hstack([[np.pi / 2], np.zeros(self.num_links - 1)])
-        self.start_vel = np.zeros(self.num_links)
+        self._start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)])
+        self._start_vel = np.zeros(self.n_links)
         self.weight_matrix_scale = 1
 
-        self._steps = 0
         self.dt = 0.01
-        # self.time_limit = 2
 
-        action_bound = np.pi * np.ones((self.num_links,))
+        action_bound = np.pi * np.ones((self.n_links,))
         state_bound = np.hstack([
-            [np.pi] * self.num_links,  # cos
-            [np.pi] * self.num_links,  # sin
-            [np.inf] * self.num_links,  # velocity
+            [np.pi] * self.n_links,  # cos
+            [np.pi] * self.n_links,  # sin
+            [np.inf] * self.n_links,  # velocity
+            [np.inf] * 2,  # x-y coordinates of via point distance
             [np.inf] * 2,  # x-y coordinates of target distance
-            [np.inf]  # env steps, because reward start after n steps TODO: Maybe
+            [np.inf]  # env steps, because reward start after n steps
         ])
         self.action_space = gym.spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape)
         self.observation_space = gym.spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
 
+        # containers for plotting
+        self.metadata = {'render.modes': ["human", "partial"]}
         self.fig = None
 
-    @property
-    def end_effector(self):
-        return self._joints[self.num_links].T
-
-    def configure(self, context):
-        pass
-
-    def reset(self):
-        self._joint_angles = self.start_pos
-        self._angle_velocity = self.start_vel
-        self._joints = np.zeros((self.num_links + 1, 2))
-        self._update_joints()
         self._steps = 0
-
-        return self._get_obs().copy()
+        self.seed()
 
     def step(self, action: np.ndarray):
         """
         a single step with an action in joint velocity space
         """
         vel = action
-        acc = (vel - self._angle_velocity) / self.dt
         self._angle_velocity = vel
         self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
-
         self._update_joints()
 
-        dist_reward = 0
-        if not self._is_collided:
-            if self._steps == 100:
-                dist_reward = np.linalg.norm(self.end_effector - self.via_point)
-            elif self._steps == 199:
-                dist_reward = np.linalg.norm(self.end_effector - self.goal_point)
+        acc = (vel - self._angle_velocity) / self.dt
+        reward, info = self._get_reward(acc)
 
-        # TODO: Do we need that?
-        reward = - dist_reward ** 2
-
-        reward -= 5e-8 * np.sum(acc**2)
-
-        if self._is_collided:
-            reward -= self.collision_penalty
-
-        info = {"is_collided": self._is_collided}
+        info.update({"is_collided": self._is_collided})
 
         self._steps += 1
-
-        # done = self._steps * self.dt > self.time_limit or self._is_collided
         done = self._is_collided
 
         return self._get_obs().copy(), reward, done, info
 
+    def reset(self):
+
+        if self.random_start:
+            # Maybe change more than dirst seed
+            first_joint = self.np_random.uniform(np.pi / 4, 3 * np.pi / 4)
+            self._joint_angles = np.hstack([[first_joint], np.zeros(self.n_links - 1)])
+            self._start_pos = self._joint_angles.copy()
+        else:
+            self._joint_angles = self._start_pos
+
+        self._generate_goal()
+
+        self._angle_velocity = self._start_vel
+        self._joints = np.zeros((self.n_links + 1, 2))
+        self._update_joints()
+        self._steps = 0
+
+        return self._get_obs().copy()
+
+    def _generate_goal(self):
+        self._via_point = self.np_random.uniform(0.5, 3.5, 2) if self._via_target is None else np.copy(self._via_target)
+        self._goal = self.np_random.uniform(0.5, 0.1, 2) if self._target is None else np.copy(self._target)
+        # raise NotImplementedError("How to properly sample points??")
+
     def _update_joints(self):
         """
         update _joints to get new end effector position. The other links are only required for rendering.
@@ -115,14 +123,38 @@ class ViaPointReacher(gym.Env):
 
         self._is_collided = self_collision
 
+    def _get_reward(self, acc):
+        success = False
+        reward = -np.inf
+        if not self._is_collided:
+            dist = np.inf
+            # return intermediate reward for via point
+            if self._steps == 100:
+                dist = np.linalg.norm(self.end_effector - self._via_point)
+            # return reward in last time step for goal
+            elif self._steps == 199:
+                dist = np.linalg.norm(self.end_effector - self._goal)
+
+            success = dist < 0.005
+        else:
+            # Episode terminates when colliding, hence return reward
+            dist = np.linalg.norm(self.end_effector - self._goal)
+            reward = -self.collision_penalty
+
+        reward -= dist ** 2
+        reward -= 5e-8 * np.sum(acc ** 2)
+        info = {"is_success": success}
+
+        return reward, info
+
     def _get_obs(self):
         theta = self._joint_angles
         return np.hstack([
             np.cos(theta),
             np.sin(theta),
             self._angle_velocity,
-            self.end_effector - self.via_point,
-            self.end_effector - self.goal_point,
+            self.end_effector - self._via_point,
+            self.end_effector - self._goal,
             self._steps
         ])
 
@@ -133,7 +165,7 @@ class ViaPointReacher(gym.Env):
 
         accumulated_theta = np.cumsum(theta, axis=0)
 
-        endeffector = np.zeros(shape=(self.num_links, num_points_per_link, 2))
+        endeffector = np.zeros(shape=(self.n_links, num_points_per_link, 2))
 
         x = np.cos(accumulated_theta) * self.link_lengths * intermediate_points
         y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points
@@ -141,33 +173,46 @@ class ViaPointReacher(gym.Env):
         endeffector[0, :, 0] = x[0, :]
         endeffector[0, :, 1] = y[0, :]
 
-        for i in range(1, self.num_links):
+        for i in range(1, self.n_links):
             endeffector[i, :, 0] = x[i, :] + endeffector[i - 1, -1, 0]
             endeffector[i, :, 1] = y[i, :] + endeffector[i - 1, -1, 1]
 
         return np.squeeze(endeffector + self._joints[0, :])
 
     def render(self, mode='human'):
+        goal_pos = self._goal.T
+        via_pos = self._via_point.T
+
         if self.fig is None:
+            # Create base figure once on the beginning. Afterwards only update
+            plt.ion()
             self.fig = plt.figure()
-            # plt.ion()
-            # plt.pause(0.01)
-        else:
-            plt.figure(self.fig.number)
+            ax = self.fig.add_subplot(1, 1, 1)
+
+            # limits
+            lim = np.sum(self.link_lengths) + 0.5
+            ax.set_xlim([-lim, lim])
+            ax.set_ylim([-lim, lim])
+
+            self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
+            self.goal_point_plot, = ax.plot(goal_pos[0], goal_pos[1], 'go')
+            self.via_point_plot, = ax.plot(via_pos[0], via_pos[1], 'gx')
+
+            self.fig.show()
+
+        self.fig.gca().set_title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}")
 
         if mode == "human":
-            plt.cla()
-            plt.title(f"Iteration: {self._steps}")
+            # goal
+            if self._steps == 1:
+                self.goal_point_plot.set_data(goal_pos[0], goal_pos[1])
+                self.via_point_plot.set_data(via_pos[0], goal_pos[1])
 
-            # Arm
-            plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
+            # arm
+            self.line.set_data(self._joints[:, 0], self._joints[:, 1])
 
-            lim = np.sum(self.link_lengths) + 0.5
-            plt.xlim([-lim, lim])
-            plt.ylim([-lim, lim])
-            # plt.draw()
-            plt.pause(1e-4)  # pushes window to foreground, which is annoying.
-            # self.fig.canvas.flush_events()
+            self.fig.canvas.draw()
+            self.fig.canvas.flush_events()
 
         elif mode == "partial":
             if self._steps == 1:
@@ -196,12 +241,39 @@ class ViaPointReacher(gym.Env):
                 # Add the patch to the Axes
                 [plt.gca().add_patch(rect) for rect in self.patches]
 
-                plt.xlim(-self.num_links, self.num_links), plt.ylim(-1, self.num_links)
+                plt.xlim(-self.n_links, self.n_links), plt.ylim(-1, self.n_links)
                 # Arm
                 plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
 
                 plt.pause(0.01)
 
+    @property
+    def active_obs(self):
+        return np.hstack([
+            [self.random_start] * self.n_links,  # cos
+            [self.random_start] * self.n_links,  # sin
+            [self.random_start] * self.n_links,  # velocity
+            [self._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 seed(self, seed=None):
+        self.np_random, seed = seeding.np_random(seed)
+        return [seed]
+
+    @property
+    def end_effector(self):
+        return self._joints[self.n_links].T
+
     def close(self):
         if self.fig is not None:
             plt.close(self.fig)
diff --git a/alr_envs/mujoco/ball_in_a_cup/utils.py b/alr_envs/mujoco/ball_in_a_cup/utils.py
index d368e45..714566a 100644
--- a/alr_envs/mujoco/ball_in_a_cup/utils.py
+++ b/alr_envs/mujoco/ball_in_a_cup/utils.py
@@ -17,19 +17,8 @@ def make_contextual_env(rank, seed=0):
     def _init():
         env = ALRBallInACupEnv(reward_type="contextual_goal")
 
-        env = DetPMPWrapper(env,
-                            num_dof=7,
-                            num_basis=5,
-                            width=0.005,
-                            policy_type="motor",
-                            start_pos=env.start_pos,
-                            duration=3.5,
-                            post_traj_time=4.5,
-                            dt=env.dt,
-                            weights_scale=0.5,
-                            zero_start=True,
-                            zero_goal=True
-                            )
+        env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
+                            policy_type="motor", weights_scale=0.5, zero_start=True, zero_goal=True)
 
         env.seed(seed + rank)
         return env
@@ -51,19 +40,8 @@ def make_env(rank, seed=0):
     def _init():
         env = ALRBallInACupEnv(reward_type="simple")
 
-        env = DetPMPWrapper(env,
-                            num_dof=7,
-                            num_basis=5,
-                            width=0.005,
-                            policy_type="motor",
-                            start_pos=env.start_pos,
-                            duration=3.5,
-                            post_traj_time=4.5,
-                            dt=env.dt,
-                            weights_scale=0.2,
-                            zero_start=True,
-                            zero_goal=True
-                            )
+        env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
+                            policy_type="motor", weights_scale=0.2, zero_start=True, zero_goal=True)
 
         env.seed(seed + rank)
         return env
@@ -85,20 +63,8 @@ def make_simple_env(rank, seed=0):
     def _init():
         env = ALRBallInACupEnv(reward_type="simple")
 
-        env = DetPMPWrapper(env,
-                            num_dof=3,
-                            num_basis=5,
-                            width=0.005,
-                            off=-0.1,
-                            policy_type="motor",
-                            start_pos=env.start_pos[1::2],
-                            duration=3.5,
-                            post_traj_time=4.5,
-                            dt=env.dt,
-                            weights_scale=0.25,
-                            zero_start=True,
-                            zero_goal=True
-                            )
+        env = DetPMPWrapper(env, num_dof=3, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
+                            policy_type="motor", weights_scale=0.25, zero_start=True, zero_goal=True, off=-0.1)
 
         env.seed(seed + rank)
         return env
diff --git a/alr_envs/mujoco/beerpong/utils.py b/alr_envs/mujoco/beerpong/utils.py
index cdcbd13..37d2ad1 100644
--- a/alr_envs/mujoco/beerpong/utils.py
+++ b/alr_envs/mujoco/beerpong/utils.py
@@ -17,19 +17,8 @@ def make_contextual_env(rank, seed=0):
     def _init():
         env = ALRBeerpongEnv()
 
-        env = DetPMPWrapper(env,
-                            num_dof=7,
-                            num_basis=5,
-                            width=0.005,
-                            policy_type="motor",
-                            start_pos=env.start_pos,
-                            duration=3.5,
-                            post_traj_time=4.5,
-                            dt=env.dt,
-                            weights_scale=0.5,
-                            zero_start=True,
-                            zero_goal=True
-                            )
+        env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
+                            policy_type="motor", weights_scale=0.5, zero_start=True, zero_goal=True)
 
         env.seed(seed + rank)
         return env
@@ -51,19 +40,8 @@ def make_env(rank, seed=0):
     def _init():
         env = ALRBeerpongEnvSimple()
 
-        env = DetPMPWrapper(env,
-                            num_dof=7,
-                            num_basis=5,
-                            width=0.005,
-                            policy_type="motor",
-                            start_pos=env.start_pos,
-                            duration=3.5,
-                            post_traj_time=4.5,
-                            dt=env.dt,
-                            weights_scale=0.25,
-                            zero_start=True,
-                            zero_goal=True
-                            )
+        env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
+                            policy_type="motor", weights_scale=0.25, zero_start=True, zero_goal=True)
 
         env.seed(seed + rank)
         return env
@@ -85,19 +63,8 @@ def make_simple_env(rank, seed=0):
     def _init():
         env = ALRBeerpongEnvSimple()
 
-        env = DetPMPWrapper(env,
-                            num_dof=3,
-                            num_basis=5,
-                            width=0.005,
-                            policy_type="motor",
-                            start_pos=env.start_pos[1::2],
-                            duration=3.5,
-                            post_traj_time=4.5,
-                            dt=env.dt,
-                            weights_scale=0.5,
-                            zero_start=True,
-                            zero_goal=True
-                            )
+        env = DetPMPWrapper(env, num_dof=3, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
+                            policy_type="motor", weights_scale=0.5, zero_start=True, zero_goal=True)
 
         env.seed(seed + rank)
         return env
diff --git a/alr_envs/utils/legacy/utils.py b/alr_envs/utils/legacy/utils.py
index fbcb34a..e96eee6 100644
--- a/alr_envs/utils/legacy/utils.py
+++ b/alr_envs/utils/legacy/utils.py
@@ -49,13 +49,13 @@ def make_holereacher_env(rank, seed=0):
     """
 
     def _init():
-        _env = hr.HoleReacher(n_links=5,
-                           allow_self_collision=False,
-                           allow_wall_collision=False,
-                           hole_width=0.25,
-                           hole_depth=1,
-                           hole_x=2,
-                           collision_penalty=100)
+        _env = hr.HoleReacherEnv(n_links=5,
+                                 allow_self_collision=False,
+                                 allow_wall_collision=False,
+                                 hole_width=0.25,
+                                 hole_depth=1,
+                                 hole_x=2,
+                                 collision_penalty=100)
 
         _env = DmpWrapper(_env,
                           num_dof=5,
@@ -89,13 +89,13 @@ def make_holereacher_fix_goal_env(rank, seed=0):
     """
 
     def _init():
-        _env = hr.HoleReacher(n_links=5,
-                           allow_self_collision=False,
-                           allow_wall_collision=False,
-                           hole_width=0.15,
-                           hole_depth=1,
-                           hole_x=1,
-                           collision_penalty=100)
+        _env = hr.HoleReacherEnv(n_links=5,
+                                 allow_self_collision=False,
+                                 allow_wall_collision=False,
+                                 hole_width=0.15,
+                                 hole_depth=1,
+                                 hole_x=1,
+                                 collision_penalty=100)
 
         _env = DmpWrapper(_env,
                           num_dof=5,
@@ -129,27 +129,16 @@ def make_holereacher_env_pmp(rank, seed=0):
     """
 
     def _init():
-        _env = hr.HoleReacher(n_links=5,
-                           allow_self_collision=False,
-                           allow_wall_collision=False,
-                           hole_width=0.15,
-                           hole_depth=1,
-                           hole_x=1,
-                           collision_penalty=1000)
+        _env = hr.HoleReacherEnv(n_links=5,
+                                 allow_self_collision=False,
+                                 allow_wall_collision=False,
+                                 hole_width=0.15,
+                                 hole_depth=1,
+                                 hole_x=1,
+                                 collision_penalty=1000)
 
-        _env = DetPMPWrapper(_env,
-                             num_dof=5,
-                             num_basis=5,
-                             width=0.02,
-                             policy_type="velocity",
-                             start_pos=_env._start_pos,
-                             duration=2,
-                             post_traj_time=0,
-                             dt=_env.dt,
-                             weights_scale=0.2,
-                             zero_start=True,
-                             zero_goal=False
-                             )
+        _env = DetPMPWrapper(_env, num_dof=5, num_basis=5, width=0.02, duration=2, dt=_env.dt, post_traj_time=0,
+                             policy_type="velocity", weights_scale=0.2, zero_start=True, zero_goal=False)
         _env.seed(seed + rank)
         return _env
 
diff --git a/alr_envs/utils/mps/detpmp_wrapper.py b/alr_envs/utils/mps/detpmp_wrapper.py
index 63de98b..3f6d1ee 100644
--- a/alr_envs/utils/mps/detpmp_wrapper.py
+++ b/alr_envs/utils/mps/detpmp_wrapper.py
@@ -7,22 +7,22 @@ from alr_envs.utils.mps.mp_wrapper import MPWrapper
 
 
 class DetPMPWrapper(MPWrapper):
-    def __init__(self, env: MPEnv, num_dof: int, num_basis: int, width: int, start_pos=None, duration: int = 1,
-                 dt: float = 0.01, post_traj_time: float = 0., policy_type: str = None, weights_scale: float = 1.,
+    def __init__(self, env: MPEnv, num_dof: int, num_basis: int, width: int, duration: int = 1, dt: float = 0.01,
+                 post_traj_time: float = 0., policy_type: str = None, weights_scale: float = 1.,
                  zero_start: bool = False, zero_goal: bool = False, **mp_kwargs):
-        # self.duration = duration  # seconds
+        self.duration = duration  # seconds
 
         super().__init__(env, num_dof, dt, duration, post_traj_time, policy_type, weights_scale, num_basis=num_basis,
-                         width=width, start_pos=start_pos, zero_start=zero_start, zero_goal=zero_goal, **mp_kwargs)
+                         width=width, zero_start=zero_start, zero_goal=zero_goal, **mp_kwargs)
+
+        self.dt = dt
 
         action_bounds = np.inf * np.ones((self.mp.n_basis * self.mp.n_dof))
         self.action_space = gym.spaces.Box(low=-action_bounds, high=action_bounds, dtype=np.float32)
 
-        self.start_pos = start_pos
-        self.dt = dt
 
     def initialize_mp(self, num_dof: int, duration: int, dt: float, num_basis: int = 5, width: float = None,
-                      start_pos: np.ndarray = None, zero_start: bool = False, zero_goal: bool = False):
+                      zero_start: bool = False, zero_goal: bool = False):
         pmp = det_promp.DeterministicProMP(n_basis=num_basis, n_dof=num_dof, width=width, off=0.01,
                                            zero_start=zero_start, zero_goal=zero_goal)
 
diff --git a/alr_envs/utils/mps/dmp_wrapper.py b/alr_envs/utils/mps/dmp_wrapper.py
index e42205e..6bc5bc6 100644
--- a/alr_envs/utils/mps/dmp_wrapper.py
+++ b/alr_envs/utils/mps/dmp_wrapper.py
@@ -63,7 +63,7 @@ class DmpWrapper(MPWrapper):
             goal_pos = params[0, -self.mp.num_dimensions:]  # [num_dof]
             params = params[:, :-self.mp.num_dimensions]  # [1,num_dof]
         else:
-            goal_pos = self.env.goal_pos  # self.mp.dmp_goal_pos.flatten()
+            goal_pos = self.env.goal_pos
             assert goal_pos is not None
 
         weight_matrix = np.reshape(params, self.mp.dmp_weights.shape)  # [num_basis, num_dof]
diff --git a/alr_envs/utils/mps/mp_environments.py b/alr_envs/utils/mps/mp_environments.py
index f720f2f..948e4ba 100644
--- a/alr_envs/utils/mps/mp_environments.py
+++ b/alr_envs/utils/mps/mp_environments.py
@@ -9,7 +9,7 @@ class MPEnv(gym.Env):
 
     @property
     @abstractmethod
-    def corrected_obs_index(self):
+    def active_obs(self):
         """Returns boolean value for each observation entry
         whether the observation is returned by the DMP for the contextual case or not.
         This effectively allows to filter unwanted or unnecessary observations from the full step-based case.
diff --git a/alr_envs/utils/mps/mp_wrapper.py b/alr_envs/utils/mps/mp_wrapper.py
index 621de00..4c92806 100644
--- a/alr_envs/utils/mps/mp_wrapper.py
+++ b/alr_envs/utils/mps/mp_wrapper.py
@@ -13,6 +13,12 @@ class MPWrapper(gym.Wrapper, ABC):
                  policy_type: str = None, weights_scale: float = 1., render_mode: str = None, **mp_kwargs):
         super().__init__(env)
 
+        # adjust observation space to reduce version
+        obs_sp = self.env.observation_space
+        self.observation_space = gym.spaces.Box(low=obs_sp.low[self.env.active_obs],
+                                                high=obs_sp.high[self.env.active_obs],
+                                                dtype=obs_sp.dtype)
+
         assert dt is not None  # this should never happen as MPWrapper is a base class
         self.post_traj_steps = int(post_traj_time / dt)
 
@@ -51,8 +57,7 @@ class MPWrapper(gym.Wrapper, ABC):
         self.env.configure(context)
 
     def reset(self):
-        obs = self.env.reset()
-        return obs[self.env]
+        return self.env.reset()[self.env.active_obs]
 
     def step(self, action: np.ndarray):
         """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step"""
@@ -82,7 +87,7 @@ class MPWrapper(gym.Wrapper, ABC):
                 break
 
         done = True
-        return obs, rewards, done, info
+        return obs[self.env.active_obs], rewards, done, info
 
     def render(self, mode='human', **kwargs):
         """Only set render options here, such that they can be used during the rollout.
diff --git a/example.py b/example.py
index 2d32ad8..166f38f 100644
--- a/example.py
+++ b/example.py
@@ -46,7 +46,7 @@ def example_dmp():
             obs = env.reset()
 
 
-def example_async(n_cpu=4, seed=int('533D', 16)):
+def example_async(env_id="alr_envs:HoleReacherDMP-v0", n_cpu=4, seed=int('533D', 16)):
     def make_env(env_id, seed, rank):
         env = gym.make(env_id)
         env.seed(seed + rank)
@@ -73,7 +73,7 @@ def example_async(n_cpu=4, seed=int('533D', 16)):
         # do not return values above threshold
         return (*map(lambda v: np.stack(v)[:n_samples], vals.values()),)
 
-    envs = gym.vector.AsyncVectorEnv([make_env("alr_envs:HoleReacherDMP-v0", seed, i) for i in range(n_cpu)])
+    envs = gym.vector.AsyncVectorEnv([make_env(env_id, seed, i) for i in range(n_cpu)])
 
     obs = envs.reset()
     print(sample(envs, 16))
@@ -82,7 +82,6 @@ def example_async(n_cpu=4, seed=int('533D', 16)):
 if __name__ == '__main__':
     # example_mujoco()
     # example_dmp()
-    # example_async()
+    example_async("alr_envs:LongSimpleReacherDMP-v0", 4)
     # env = gym.make("alr_envs:HoleReacherDMP-v0", context=0.1)
-    env = gym.make("alr_envs:SimpleReacherDMP-v1")
-    print()
\ No newline at end of file
+    # env = gym.make("alr_envs:HoleReacherDMP-v1")