Adopt new interface structure

README.md

@@ -3,7 +3,8 @@
 This repository collects custom Robotics environments not included in benchmark suites like OpenAI gym, rllab, etc. 
 Creating a custom (Mujoco) gym environment can be done according to [this guide](https://github.com/openai/gym/blob/master/docs/creating-environments.md).
 For stochastic search problems with gym interface use the `Rosenbrock-v0` reference implementation.
-We also support to solve environments with DMPs. When adding new DMP tasks check the `ViaPointReacherDMP-v0` reference implementation.
+We also support to solve environments with Dynamic Movement Primitives (DMPs) and Probabilistic Movement Primitives (DetPMP, we only consider the mean usually). 
+When adding new DMP tasks check the `ViaPointReacherDMP-v0` reference implementation.
 When simply using the tasks, you can also leverage the wrapper class `DmpWrapper` to turn normal gym environments in to DMP tasks.
 
 ## Environments

alr_envs/open_ai/continuous_mountain_car/mp_wrapper.py

@@ -1,7 +1,7 @@
 from typing import Union
 
 import numpy as np
-from mp_env_api.env_wrappers.mp_env_wrapper import MPEnvWrapper
+from mp_env_api.interface_wrappers.mp_env_wrapper import MPEnvWrapper
 
 
 class MPWrapper(MPEnvWrapper):

alr_envs/open_ai/fetch/mp_wrapper.py

@@ -1,7 +1,7 @@
 from typing import Union
 
 import numpy as np
-from mp_env_api.env_wrappers.mp_env_wrapper import MPEnvWrapper
+from mp_env_api.interface_wrappers.mp_env_wrapper import MPEnvWrapper
 
 
 class MPWrapper(MPEnvWrapper):

alr_envs/open_ai/reacher_v2/mp_wrapper.py

@@ -1,7 +1,7 @@
 from typing import Union
 
 import numpy as np
-from mp_env_api.env_wrappers.mp_env_wrapper import MPEnvWrapper
+from mp_env_api.interface_wrappers.mp_env_wrapper import MPEnvWrapper
 
 
 class MPWrapper(MPEnvWrapper):
@@ -14,10 +14,6 @@ class MPWrapper(MPEnvWrapper):
     def current_pos(self) -> Union[float, int, np.ndarray]:
         return self.sim.data.qpos[:2]
 
-    @property
-    def goal_pos(self):
-        return self.goal
-
     @property
     def dt(self) -> Union[float, int]:
         return self.env.dt

reacher.egg-info/PKG-INFO

@@ -1,10 +0,0 @@
-Metadata-Version: 1.0
-Name: reacher
-Version: 0.0.1
-Summary: UNKNOWN
-Home-page: UNKNOWN
-Author: UNKNOWN
-Author-email: UNKNOWN
-License: UNKNOWN
-Description: UNKNOWN
-Platform: UNKNOWN

reacher.egg-info/SOURCES.txt

@@ -1,7 +0,0 @@
-README.md
-setup.py
-reacher.egg-info/PKG-INFO
-reacher.egg-info/SOURCES.txt
-reacher.egg-info/dependency_links.txt
-reacher.egg-info/requires.txt
-reacher.egg-info/top_level.txt

reacher.egg-info/dependency_links.txt

@@ -1 +0,0 @@
-

reacher.egg-info/requires.txt

@@ -1 +0,0 @@
-gym

reacher.egg-info/top_level.txt

@@ -1 +0,0 @@
-

setup.py

@@ -3,14 +3,14 @@ from setuptools import setup
 setup(
     name='alr_envs',
     version='0.0.1',
-    packages=['alr_envs', 'alr_envs.classic_control', 'alr_envs.mujoco', 'alr_envs.stochastic_search',
+    packages=['alr_envs', 'alr_envs.classic_control', 'alr_envs.open_ai', 'alr_envs.mujoco', 'alr_envs.stochastic_search',
               'alr_envs.utils'],
     install_requires=[
         'gym',
         'PyQt5',
         'matplotlib',
         'mp_env_api @ git+ssh://git@github.com/ALRhub/motion_primitive_env_api.git',
-        'mujoco_py',
+        'mujoco-py<2.1,>=2.0',
         'dm_control'
     ],