updated for gym 0.25.1

fancy_gym/black_box/black_box_wrapper.py

@@ -86,8 +86,8 @@ class BlackBoxWrapper(gym.ObservationWrapper):
         velocity = get_numpy(self.traj_gen.get_traj_vel())
 
         # Remove first element of trajectory as this is the current position and velocity
-        trajectory = trajectory[1:]
+        # trajectory = trajectory[1:]
-        velocity = velocity[1:]
+        # velocity = velocity[1:]
 
         return trajectory, velocity
 

fancy_gym/black_box/raw_interface_wrapper.py

@@ -1,4 +1,3 @@
-from abc import abstractmethod
 from typing import Union, Tuple
 
 import gym
@@ -23,7 +22,6 @@ class RawInterfaceWrapper(gym.Wrapper):
         return np.ones(self.env.observation_space.shape[0], dtype=bool)
 
     @property
-    @abstractmethod
     def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
         """
             Returns the current position of the action/control dimension.
@@ -32,10 +30,9 @@ class RawInterfaceWrapper(gym.Wrapper):
             it should, however, be implemented regardless.
             E.g. The joint positions that are directly or indirectly controlled by the action.
         """
-        raise NotImplementedError()
+        raise NotImplementedError
 
     @property
-    @abstractmethod
     def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
         """
             Returns the current velocity of the action/control dimension.
@@ -44,7 +41,7 @@ class RawInterfaceWrapper(gym.Wrapper):
             it should, however, be implemented regardless.
             E.g. The joint velocities that are directly or indirectly controlled by the action.
         """
-        raise NotImplementedError()
+        raise NotImplementedError
 
     @property
     def dt(self) -> float:

fancy_gym/envs/__init__.py

@@ -126,7 +126,7 @@ for _dims in [5, 7]:
     register(
         id=f'Reacher{_dims}dSparse-v0',
         entry_point='fancy_gym.envs.mujoco:ReacherEnv',
-        max_episode_steps=200,
+        max_episode_steps=MAX_EPISODE_STEPS_REACHER,
         kwargs={
             "sparse": True,
             'reward_weight': 200,

fancy_gym/envs/classic_control/base_reacher/base_reacher.py

@@ -1,4 +1,3 @@
-from abc import ABC, abstractmethod
 from typing import Union, Tuple, Optional
 
 import gym
@@ -10,7 +9,7 @@ from gym.utils import seeding
 from fancy_gym.envs.classic_control.utils import intersect
 
 
-class BaseReacherEnv(gym.Env, ABC):
+class BaseReacherEnv(gym.Env):
     """
     Base class for all reaching environments.
     """
@@ -87,13 +86,6 @@ class BaseReacherEnv(gym.Env, ABC):
 
         return self._get_obs().copy()
 
-    @abstractmethod
-    def step(self, action: np.ndarray):
-        """
-        A single step with action in angular velocity space
-        """
-        raise NotImplementedError
-
     def _update_joints(self):
         """
         update joints to get new end-effector position. The other links are only required for rendering.
@@ -120,27 +112,24 @@ class BaseReacherEnv(gym.Env, ABC):
                     return True
         return False
 
-    @abstractmethod
     def _get_reward(self, action: np.ndarray) -> (float, dict):
-        pass
+        raise NotImplementedError
 
-    @abstractmethod
     def _get_obs(self) -> np.ndarray:
-        pass
+        raise NotImplementedError
 
-    @abstractmethod
     def _check_collisions(self) -> bool:
-        pass
+        raise NotImplementedError
 
-    @abstractmethod
     def _terminate(self, info) -> bool:
-        return False
+        raise NotImplementedError
 
     def seed(self, seed=None):
         self.np_random, seed = seeding.np_random(seed)
         return [seed]
 
     def close(self):
+        super(BaseReacherEnv, self).close()
         del self.fig
 
     @property

fancy_gym/envs/classic_control/base_reacher/base_reacher_direct.py

@@ -1,12 +1,10 @@
-from abc import ABC
-
 import numpy as np
 from gym import spaces
 
 from fancy_gym.envs.classic_control.base_reacher.base_reacher import BaseReacherEnv
 
 
-class BaseReacherDirectEnv(BaseReacherEnv, ABC):
+class BaseReacherDirectEnv(BaseReacherEnv):
     """
     Base class for directly controlled reaching environments
     """

fancy_gym/envs/classic_control/base_reacher/base_reacher_torque.py

@@ -1,12 +1,10 @@
-from abc import ABC
-
 import numpy as np
 from gym import spaces
 
 from fancy_gym.envs.classic_control.base_reacher.base_reacher import BaseReacherEnv
 
 
-class BaseReacherTorqueEnv(BaseReacherEnv, ABC):
+class BaseReacherTorqueEnv(BaseReacherEnv):
     """
     Base class for torque controlled reaching environments
     """

fancy_gym/envs/mujoco/hopper_jump/hopper_jump.py

@@ -98,7 +98,7 @@ class HopperJumpEnv(HopperEnv):
         if not self.sparse or (self.sparse and self._steps >= MAX_EPISODE_STEPS_HOPPERJUMP):
             healthy_reward = self.healthy_reward
             distance_reward = -goal_dist * self._dist_weight
-            height_reward = (self.max_height if self.sparse else self.get_body_com("torso")[2]) * self._height_weight
+            height_reward = (self.max_height if self.sparse else height_after) * self._height_weight
             contact_reward = -(self.contact_dist or 5) * self._contact_weight
             rewards = self._forward_reward_weight * (distance_reward + height_reward + contact_reward + healthy_reward)
 
@@ -123,7 +123,7 @@ class HopperJumpEnv(HopperEnv):
         return np.concatenate((super(HopperJumpEnv, self)._get_obs(), goal_dist.copy(), self.goal[:1]))
 
     def reset_model(self):
-        super(HopperJumpEnv, self).reset_model()
+        # super(HopperJumpEnv, self).reset_model()
 
         # self.goal = self.np_random.uniform(0.3, 1.35, 1)[0]
         self.goal = np.concatenate([self.np_random.uniform(0.3, 1.35, 1), np.zeros(2, )])
@@ -176,76 +176,76 @@ class HopperJumpEnv(HopperEnv):
                 return True
         return False
 
-# TODO is that needed? if so test it
+# # TODO is that needed? if so test it
-class HopperJumpStepEnv(HopperJumpEnv):
+# class HopperJumpStepEnv(HopperJumpEnv):
-
+#
-    def __init__(self,
+#     def __init__(self,
-                 xml_file='hopper_jump.xml',
+#                  xml_file='hopper_jump.xml',
-                 forward_reward_weight=1.0,
+#                  forward_reward_weight=1.0,
-                 ctrl_cost_weight=1e-3,
+#                  ctrl_cost_weight=1e-3,
-                 healthy_reward=1.0,
+#                  healthy_reward=1.0,
-                 height_weight=3,
+#                  height_weight=3,
-                 dist_weight=3,
+#                  dist_weight=3,
-                 terminate_when_unhealthy=False,
+#                  terminate_when_unhealthy=False,
-                 healthy_state_range=(-100.0, 100.0),
+#                  healthy_state_range=(-100.0, 100.0),
-                 healthy_z_range=(0.5, float('inf')),
+#                  healthy_z_range=(0.5, float('inf')),
-                 healthy_angle_range=(-float('inf'), float('inf')),
+#                  healthy_angle_range=(-float('inf'), float('inf')),
-                 reset_noise_scale=5e-3,
+#                  reset_noise_scale=5e-3,
-                 exclude_current_positions_from_observation=False
+#                  exclude_current_positions_from_observation=False
-                 ):
+#                  ):
-
+#
-        self._height_weight = height_weight
+#         self._height_weight = height_weight
-        self._dist_weight = dist_weight
+#         self._dist_weight = dist_weight
-        super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy,
+#         super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy,
-                         healthy_state_range, healthy_z_range, healthy_angle_range, reset_noise_scale,
+#                          healthy_state_range, healthy_z_range, healthy_angle_range, reset_noise_scale,
-                         exclude_current_positions_from_observation)
+#                          exclude_current_positions_from_observation)
-
+#
-    def step(self, action):
+#     def step(self, action):
-        self._steps += 1
+#         self._steps += 1
-
+#
-        self.do_simulation(action, self.frame_skip)
+#         self.do_simulation(action, self.frame_skip)
-
+#
-        height_after = self.get_body_com("torso")[2]
+#         height_after = self.get_body_com("torso")[2]
-        site_pos_after = self.data.site('foot_site').xpos.copy()
+#         site_pos_after = self.data.site('foot_site').xpos.copy()
-        self.max_height = max(height_after, self.max_height)
+#         self.max_height = max(height_after, self.max_height)
-
+#
-        ctrl_cost = self.control_cost(action)
+#         ctrl_cost = self.control_cost(action)
-        healthy_reward = self.healthy_reward
+#         healthy_reward = self.healthy_reward
-        height_reward = self._height_weight * height_after
+#         height_reward = self._height_weight * height_after
-        goal_dist = np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0]))
+#         goal_dist = np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0]))
-        goal_dist_reward = -self._dist_weight * goal_dist
+#         goal_dist_reward = -self._dist_weight * goal_dist
-        dist_reward = self._forward_reward_weight * (goal_dist_reward + height_reward)
+#         dist_reward = self._forward_reward_weight * (goal_dist_reward + height_reward)
-
+#
-        rewards = dist_reward + healthy_reward
+#         rewards = dist_reward + healthy_reward
-        costs = ctrl_cost
+#         costs = ctrl_cost
-        done = False
+#         done = False
-
+#
-        # This is only for logging the distance to goal when first having the contact
+#         # This is only for logging the distance to goal when first having the contact
-        has_floor_contact = self._is_floor_foot_contact() if not self.contact_with_floor else False
+#         has_floor_contact = self._is_floor_foot_contact() if not self.contact_with_floor else False
-
+#
-        if not self.init_floor_contact:
+#         if not self.init_floor_contact:
-            self.init_floor_contact = has_floor_contact
+#             self.init_floor_contact = has_floor_contact
-        if self.init_floor_contact and not self.has_left_floor:
+#         if self.init_floor_contact and not self.has_left_floor:
-            self.has_left_floor = not has_floor_contact
+#             self.has_left_floor = not has_floor_contact
-        if not self.contact_with_floor and self.has_left_floor:
+#         if not self.contact_with_floor and self.has_left_floor:
-            self.contact_with_floor = has_floor_contact
+#             self.contact_with_floor = has_floor_contact
-
+#
-        if self.contact_dist is None and self.contact_with_floor:
+#         if self.contact_dist is None and self.contact_with_floor:
-            self.contact_dist = goal_dist
+#             self.contact_dist = goal_dist
-
+#
-        ##############################################################
+#         ##############################################################
-
+#
-        observation = self._get_obs()
+#         observation = self._get_obs()
-        reward = rewards - costs
+#         reward = rewards - costs
-        info = {
+#         info = {
-            'height': height_after,
+#             'height': height_after,
-            'x_pos': site_pos_after,
+#             'x_pos': site_pos_after,
-            'max_height': copy.copy(self.max_height),
+#             'max_height': copy.copy(self.max_height),
-            'goal': copy.copy(self.goal),
+#             'goal': copy.copy(self.goal),
-            'goal_dist': goal_dist,
+#             'goal_dist': goal_dist,
-            'height_rew': height_reward,
+#             'height_rew': height_reward,
-            'healthy_reward': healthy_reward,
+#             'healthy_reward': healthy_reward,
-            'healthy': copy.copy(self.is_healthy),
+#             'healthy': copy.copy(self.is_healthy),
-            'contact_dist': copy.copy(self.contact_dist) or 0
+#             'contact_dist': copy.copy(self.contact_dist) or 0
-        }
+#         }
-        return observation, reward, done, info
+#         return observation, reward, done, info

fancy_gym/envs/mujoco/hopper_jump/mp_wrapper.py

@@ -14,7 +14,7 @@ class MPWrapper(RawInterfaceWrapper):
             [False] * (2 + int(not self.exclude_current_positions_from_observation)),  # position
             [True] * 3,  # set to true if randomize initial pos
             [False] * 6,  # velocity
-            [True] * 3,  # goal distance
+            [False] * 3,  # goal distance
             [True]  # goal
         ])
 

fancy_gym/meta/base_metaworld_mp_wrapper.py

@@ -1,4 +1,3 @@
-from abc import ABC
 from typing import Tuple, Union
 
 import numpy as np
@@ -6,7 +5,7 @@ import numpy as np
 from fancy_gym.black_box.raw_interface_wrapper import RawInterfaceWrapper
 
 
-class BaseMetaworldMPWrapper(RawInterfaceWrapper, ABC):
+class BaseMetaworldMPWrapper(RawInterfaceWrapper):
     @property
     def current_pos(self) -> Union[float, int, np.ndarray]:
         r_close = self.env.data.get_joint_qpos("r_close")

setup.py

@@ -6,7 +6,9 @@ from setuptools import setup, find_packages
 extras = {
     "dmc": ["dm_control>=1.0.1"],
     "metaworld": ["metaworld @ git+https://github.com/rlworkgroup/metaworld.git@master#egg=metaworld",
-                  'mujoco-py<2.2,>=2.1'],
+                  'mujoco-py<2.2,>=2.1',
+                  'scipy'
+                  ],
 }
 
 # All dependencies