updated custom tasks to new api

fancy_gym/black_box/black_box_wrapper.py

@@ -1,8 +1,9 @@
-from typing import Tuple, Optional
+from typing import Tuple, Optional, Dict, Any
 
-import gym
+import gymnasium as gym
 import numpy as np
-from gym import spaces
+from gymnasium import spaces
+from gymnasium.core import ObsType
 from mp_pytorch.mp.mp_interfaces import MPInterface
 
 from fancy_gym.black_box.controller.base_controller import BaseController
@@ -140,7 +141,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
         for t, (pos, vel) in enumerate(zip(trajectory, velocity)):
             step_action = self.tracking_controller.get_action(pos, vel, self.current_pos, self.current_vel)
             c_action = np.clip(step_action, self.env.action_space.low, self.env.action_space.high)
-            obs, c_reward, done, info = self.env.step(c_action)
+            obs, c_reward, terminated, truncated, info = self.env.step(c_action)
             rewards[t] = c_reward
 
             if self.verbose >= 2:
@@ -155,7 +156,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
             if self.render_kwargs:
                 self.env.render(**self.render_kwargs)
 
-            if done or self.replanning_schedule(self.current_pos, self.current_vel, obs, c_action,
+            if terminated or truncated or self.replanning_schedule(self.current_pos, self.current_vel, obs, c_action,
                                                                    t + 1 + self.current_traj_steps):
                 break
 
@@ -171,13 +172,14 @@ class BlackBoxWrapper(gym.ObservationWrapper):
 
         infos['trajectory_length'] = t + 1
         trajectory_return = self.reward_aggregation(rewards[:t + 1])
-        return self.observation(obs), trajectory_return, done, infos
+        return self.observation(obs), trajectory_return, terminated, truncated, infos
 
     def render(self, **kwargs):
         """Only set render options here, such that they can be used during the rollout.
         This only needs to be called once"""
         self.render_kwargs = kwargs
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None):
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
+            -> Tuple[ObsType, Dict[str, Any]]:
         self.current_traj_steps = 0
-        return super(BlackBoxWrapper, self).reset()
+        return super(BlackBoxWrapper, self).reset(seed=seed, options=options)

fancy_gym/black_box/raw_interface_wrapper.py

@@ -1,6 +1,6 @@
 from typing import Union, Tuple
 
-import gym
+import gymnasium as gym
 import numpy as np
 from mp_pytorch.mp.mp_interfaces import MPInterface
 

fancy_gym/envs/__init__.py

@@ -1,7 +1,7 @@
 from copy import deepcopy
 
 import numpy as np
-from gym import register
+from gymnasium import register
 
 from . import classic_control, mujoco
 from .classic_control.hole_reacher.hole_reacher import HoleReacherEnv

fancy_gym/envs/classic_control/base_reacher/base_reacher.py

@@ -1,10 +1,10 @@
-from typing import Union, Tuple, Optional
+from typing import Union, Tuple, Optional, Any, Dict
 
-import gym
+import gymnasium as gym
 import numpy as np
-from gym import spaces
+from gymnasium import spaces
-from gym.core import ObsType
+from gymnasium.core import ObsType
-from gym.utils import seeding
+from gymnasium.utils import seeding
 
 from fancy_gym.envs.classic_control.utils import intersect
 
@@ -69,10 +69,14 @@ class BaseReacherEnv(gym.Env):
     def current_vel(self):
         return self._angle_velocity.copy()
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False,
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
-              options: Optional[dict] = None, ) -> Union[ObsType, Tuple[ObsType, dict]]:
+            -> Tuple[ObsType, Dict[str, Any]]:
         # Sample only orientation of first link, i.e. the arm is always straight.
-        if self.random_start:
+        try:
+            random_start = options.get('random_start', self.random_start)
+        except AttributeError:
+            random_start = self.random_start
+        if random_start:
             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()
@@ -84,7 +88,7 @@ class BaseReacherEnv(gym.Env):
         self._update_joints()
         self._steps = 0
 
-        return self._get_obs().copy()
+        return self._get_obs().copy(), {}
 
     def _update_joints(self):
         """

fancy_gym/envs/classic_control/base_reacher/base_reacher_direct.py

@@ -1,5 +1,5 @@
 import numpy as np
-from gym import spaces
+from gymnasium import spaces
 
 from fancy_gym.envs.classic_control.base_reacher.base_reacher import BaseReacherEnv
 
@@ -32,6 +32,7 @@ class BaseReacherDirectEnv(BaseReacherEnv):
         reward, info = self._get_reward(action)
 
         self._steps += 1
-        done = self._terminate(info)
+        terminated = self._terminate(info)
+        truncated = False
 
-        return self._get_obs().copy(), reward, done, info
+        return self._get_obs().copy(), reward, terminated, truncated, info

fancy_gym/envs/classic_control/base_reacher/base_reacher_torque.py

@@ -1,5 +1,5 @@
 import numpy as np
-from gym import spaces
+from gymnasium import spaces
 
 from fancy_gym.envs.classic_control.base_reacher.base_reacher import BaseReacherEnv
 
@@ -31,6 +31,7 @@ class BaseReacherTorqueEnv(BaseReacherEnv):
         reward, info = self._get_reward(action)
 
         self._steps += 1
-        done = False
+        terminated = False
+        truncated = False
 
-        return self._get_obs().copy(), reward, done, info
+        return self._get_obs().copy(), reward, terminated, truncated, info

fancy_gym/envs/classic_control/hole_reacher/hole_reacher.py

@@ -1,9 +1,10 @@
-from typing import Union, Optional, Tuple
+from typing import Union, Optional, Tuple, Any, Dict
 
-import gym
+import gymnasium as gym
 import matplotlib.pyplot as plt
 import numpy as np
-from gym.core import ObsType
+from gymnasium import spaces
+from gymnasium.core import ObsType
 from matplotlib import patches
 
 from fancy_gym.envs.classic_control.base_reacher.base_reacher_direct import BaseReacherDirectEnv
@@ -40,7 +41,7 @@ class HoleReacherEnv(BaseReacherDirectEnv):
             [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)
+        self.observation_space = spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
 
         if rew_fct == "simple":
             from fancy_gym.envs.classic_control.hole_reacher.hr_simple_reward import HolereacherReward
@@ -54,8 +55,8 @@ class HoleReacherEnv(BaseReacherDirectEnv):
         else:
             raise ValueError("Unknown reward function {}".format(rew_fct))
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False,
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
-              options: Optional[dict] = None, ) -> Union[ObsType, Tuple[ObsType, dict]]:
+            -> Tuple[ObsType, Dict[str, Any]]:
         self._generate_hole()
         self._set_patches()
         self.reward_function.reset()
@@ -225,14 +226,4 @@ class HoleReacherEnv(BaseReacherDirectEnv):
             self.fig.gca().add_patch(hole_floor)
 
 
-if __name__ == "__main__":
 
-    env = HoleReacherEnv(5)
-    env.reset()
-
-    for i in range(10000):
-        ac = env.action_space.sample()
-        obs, rew, done, info = env.step(ac)
-        env.render()
-        if done:
-            env.reset()

fancy_gym/envs/classic_control/simple_reacher/simple_reacher.py

@@ -1,9 +1,9 @@
-from typing import Iterable, Union, Optional, Tuple
+from typing import Iterable, Union, Optional, Tuple, Any, Dict
 
 import matplotlib.pyplot as plt
 import numpy as np
-from gym import spaces
+from gymnasium import spaces
-from gym.core import ObsType
+from gymnasium.core import ObsType
 
 from fancy_gym.envs.classic_control.base_reacher.base_reacher_torque import BaseReacherTorqueEnv
 
@@ -42,11 +42,10 @@ class SimpleReacherEnv(BaseReacherTorqueEnv):
     # def start_pos(self):
     #     return self._start_pos
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False,
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
-              options: Optional[dict] = None, ) -> Union[ObsType, Tuple[ObsType, dict]]:
+            -> Tuple[ObsType, Dict[str, Any]]:
         self._generate_goal()
-
+        return super().reset(seed=seed, options=options)
-        return super().reset()
 
     def _get_reward(self, action: np.ndarray):
         diff = self.end_effector - self._goal
@@ -128,14 +127,3 @@ class SimpleReacherEnv(BaseReacherTorqueEnv):
         self.fig.canvas.draw()
         self.fig.canvas.flush_events()
 
-
-if __name__ == "__main__":
-    env = SimpleReacherEnv(5)
-    env.reset()
-    for i in range(200):
-        ac = env.action_space.sample()
-        obs, rew, done, info = env.step(ac)
-
-        env.render()
-        if done:
-            break

fancy_gym/envs/classic_control/viapoint_reacher/viapoint_reacher.py

@@ -1,9 +1,10 @@
-from typing import Iterable, Union, Tuple, Optional
+from typing import Iterable, Union, Tuple, Optional, Any, Dict
 
-import gym
+import gymnasium as gym
 import matplotlib.pyplot as plt
 import numpy as np
-from gym.core import ObsType
+from gymnasium import spaces
+from gymnasium.core import ObsType
 
 from fancy_gym.envs.classic_control.base_reacher.base_reacher_direct import BaseReacherDirectEnv
 
@@ -34,16 +35,16 @@ class ViaPointReacherEnv(BaseReacherDirectEnv):
             [np.inf] * 2,  # x-y coordinates of target distance
             [np.inf]  # env steps, because reward start after n steps
         ])
-        self.observation_space = gym.spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
+        self.observation_space = spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
 
     # @property
     # def start_pos(self):
     #     return self._start_pos
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False,
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
-              options: Optional[dict] = None, ) -> Union[ObsType, Tuple[ObsType, dict]]:
+            -> Tuple[ObsType, Dict[str, Any]]:
         self._generate_goal()
-        return super().reset()
+        return super().reset(seed=seed, options=options)
 
     def _generate_goal(self):
         # TODO: Maybe improve this later, this can yield quite a lot of invalid settings
@@ -185,14 +186,3 @@ class ViaPointReacherEnv(BaseReacherDirectEnv):
                 plt.pause(0.01)
 
 
-if __name__ == "__main__":
-
-    env = ViaPointReacherEnv(5)
-    env.reset()
-
-    for i in range(10000):
-        ac = env.action_space.sample()
-        obs, rew, done, info = env.step(ac)
-        env.render()
-        if done:
-            env.reset()

fancy_gym/envs/mujoco/ant_jump/ant_jump.py

@@ -1,8 +1,8 @@
-from typing import Tuple, Union, Optional
+from typing import Tuple, Union, Optional, Any, Dict
 
 import numpy as np
-from gym.core import ObsType
+from gymnasium.core import ObsType
-from gym.envs.mujoco.ant_v4 import AntEnv
+from gymnasium.envs.mujoco.ant_v4 import AntEnv
 
 MAX_EPISODE_STEPS_ANTJUMP = 200
 
@@ -61,9 +61,10 @@ class AntJumpEnv(AntEnv):
 
         costs = ctrl_cost + contact_cost
 
-        done = bool(height < 0.3)  # fall over -> is the 0.3 value from healthy_z_range? TODO change 0.3 to the value of healthy z angle
+        terminated = bool(
+            height < 0.3)  # fall over -> is the 0.3 value from healthy_z_range? TODO change 0.3 to the value of healthy z angle
 
-        if self.current_step == MAX_EPISODE_STEPS_ANTJUMP or done:
+        if self.current_step == MAX_EPISODE_STEPS_ANTJUMP or terminated:
             # -10 for scaling the value of the distance between the max_height and the goal height; only used when context is enabled
             # height_reward = -10 * (np.linalg.norm(self.max_height - self.goal))
             height_reward = -10 * np.linalg.norm(self.max_height - self.goal)
@@ -80,19 +81,20 @@ class AntJumpEnv(AntEnv):
             'max_height': self.max_height,
             'goal': self.goal
         }
+        truncated = False
 
-        return obs, reward, done, info
+        return obs, reward, terminated, truncated, info
 
     def _get_obs(self):
         return np.append(super()._get_obs(), self.goal)
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False,
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
-              options: Optional[dict] = None, ) -> Union[ObsType, Tuple[ObsType, dict]]:
+            -> Tuple[ObsType, Dict[str, Any]]:
         self.current_step = 0
         self.max_height = 0
         # goal heights from 1.0 to 2.5; can be increased, but didnt work well with CMORE
         self.goal = self.np_random.uniform(1.0, 2.5, 1)
-        return super().reset()
+        return super().reset(seed=seed, options=options)
 
     # reset_model had to be implemented in every env to make it deterministic
     def reset_model(self):

fancy_gym/envs/mujoco/beerpong/beerpong.py

@@ -1,9 +1,10 @@
 import os
-from typing import Optional
+from typing import Optional, Any, Dict, Tuple
 
 import numpy as np
-from gym import utils
+from gymnasium import utils
-from gym.envs.mujoco import MujocoEnv
+from gymnasium.core import ObsType
+from gymnasium.envs.mujoco import MujocoEnv
 
 MAX_EPISODE_STEPS_BEERPONG = 300
 FIXED_RELEASE_STEP = 62  # empirically evaluated for frame_skip=2!
@@ -30,7 +31,7 @@ CUP_COLLISION_OBJ = ["cup_geom_table3", "cup_geom_table4", "cup_geom_table5", "c
 
 
 class BeerPongEnv(MujocoEnv, utils.EzPickle):
-    def __init__(self):
+    def __init__(self, **kwargs):
         self._steps = 0
         # Small Context -> Easier. Todo: Should we do different versions?
         # self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "beerpong_wo_cup.xml")
@@ -65,7 +66,13 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
         self.ball_in_cup = False
         self.dist_ground_cup = -1  # distance floor to cup if first floor contact
 
-        MujocoEnv.__init__(self, model_path=self.xml_path, frame_skip=1, mujoco_bindings="mujoco")
+        MujocoEnv.__init__(
+            self,
+            self.xml_path,
+            frame_skip=1,
+            observation_space=self.observation_space,
+            **kwargs
+        )
         utils.EzPickle.__init__(self)
 
     @property
@@ -76,7 +83,8 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
     def start_vel(self):
         return self._start_vel
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None):
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
+            -> Tuple[ObsType, Dict[str, Any]]:
         self.dists = []
         self.dists_final = []
         self.action_costs = []
@@ -86,7 +94,7 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
         self.ball_cup_contact = False
         self.ball_in_cup = False
         self.dist_ground_cup = -1  # distance floor to cup if first floor contact
-        return super().reset()
+        return super().reset(seed=seed, options=options)
 
     def reset_model(self):
         init_pos_all = self.init_qpos.copy()
@@ -128,11 +136,11 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
         if not crash:
             reward, reward_infos = self._get_reward(applied_action)
             is_collided = reward_infos['is_collided']  # TODO: Remove if self collision does not make a difference
-            done = is_collided
+            terminated = is_collided
             self._steps += 1
         else:
             reward = -30
-            done = True
+            terminated = True
             reward_infos = {"success": False, "ball_pos": np.zeros(3), "ball_vel": np.zeros(3), "is_collided": False}
 
         infos = dict(
@@ -142,7 +150,10 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
             q_vel=self.data.qvel[0:7].ravel().copy(), sim_crash=crash,
         )
         infos.update(reward_infos)
-        return ob, reward, done, infos
+
+        truncated = False
+
+        return ob, reward, terminated, truncated, infos
 
     def _get_obs(self):
         theta = self.data.qpos.flat[:7].copy()
@@ -258,9 +269,9 @@ class BeerPongEnvStepBasedEpisodicReward(BeerPongEnv):
             return super(BeerPongEnvStepBasedEpisodicReward, self).step(a)
         else:
             reward = 0
-            done = True
+            terminated, truncated = True, False
             while self._steps < MAX_EPISODE_STEPS_BEERPONG:
-                obs, sub_reward, done, infos = super(BeerPongEnvStepBasedEpisodicReward, self).step(
+                obs, sub_reward, terminated, truncated, infos = super(BeerPongEnvStepBasedEpisodicReward, self).step(
                     np.zeros(a.shape))
                 reward += sub_reward
-        return obs, reward, done, infos
+        return obs, reward, terminated, truncated, infos

fancy_gym/envs/mujoco/beerpong/deprecated/beerpong.py

@@ -2,8 +2,8 @@ import os
 
 import mujoco_py.builder
 import numpy as np
-from gym import utils
+from gymnasium import utils
-from gym.envs.mujoco import MujocoEnv
+from gymnasium.envs.mujoco import MujocoEnv
 
 from fancy_gym.envs.mujoco.beerpong.deprecated.beerpong_reward_staged import BeerPongReward
 
@@ -90,11 +90,11 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
         if not crash:
             reward, reward_infos = self.reward_function.compute_reward(self, applied_action)
             is_collided = reward_infos['is_collided']
-            done = is_collided or self._steps == self.ep_length - 1
+            terminated = is_collided or self._steps == self.ep_length - 1
             self._steps += 1
         else:
             reward = -30
-            done = True
+            terminated = True
             reward_infos = {"success": False, "ball_pos": np.zeros(3), "ball_vel": np.zeros(3), "is_collided": False}
 
         infos = dict(
@@ -104,7 +104,7 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
             q_vel=self.sim.data.qvel[0:7].ravel().copy(), sim_crash=crash,
         )
         infos.update(reward_infos)
-        return ob, reward, done, infos
+        return ob, reward, terminated, infos
 
     def _get_obs(self):
         theta = self.sim.data.qpos.flat[:7]
@@ -143,16 +143,16 @@ class BeerPongEnvStepBasedEpisodicReward(BeerPongEnv):
             return super(BeerPongEnvStepBasedEpisodicReward, self).step(a)
         else:
             reward = 0
-            done = False
+            terminated, truncated = False, False
-            while not done:
+            while not (terminated or truncated):
-                sub_ob, sub_reward, done, sub_infos = super(BeerPongEnvStepBasedEpisodicReward, self).step(
+                sub_ob, sub_reward, terminated, truncated, sub_infos = super(BeerPongEnvStepBasedEpisodicReward,
-                    np.zeros(a.shape))
+                                                                             self).step(np.zeros(a.shape))
                 reward += sub_reward
             infos = sub_infos
             ob = sub_ob
             ob[-1] = self.release_step + 1  # Since we simulate until the end of the episode, PPO does not see the
             # internal steps and thus, the observation also needs to be set correctly
-        return ob, reward, done, infos
+        return ob, reward, terminated, truncated, infos
 
 
 # class BeerBongEnvStepBased(BeerBongEnv):
@@ -186,27 +186,3 @@ class BeerPongEnvStepBasedEpisodicReward(BeerPongEnv):
 #             ob[-1] = self.release_step + 1  # Since we simulate until the end of the episode, PPO does not see the
 #             # internal steps and thus, the observation also needs to be set correctly
 #         return ob, reward, done, infos
-
-
-if __name__ == "__main__":
-    env = BeerPongEnv(frame_skip=2)
-    env.seed(0)
-    # env = BeerBongEnvStepBased(frame_skip=2)
-    # env = BeerBongEnvStepBasedEpisodicReward(frame_skip=2)
-    # env = BeerBongEnvFixedReleaseStep(frame_skip=2)
-    import time
-
-    env.reset()
-    env.render("human")
-    for i in range(600):
-        # ac = 10 * env.action_space.sample()
-        ac = 0.05 * np.ones(7)
-        obs, rew, d, info = env.step(ac)
-        env.render("human")
-
-        if d:
-            print('reward:', rew)
-            print('RESETTING')
-            env.reset()
-            time.sleep(1)
-    env.close()

fancy_gym/envs/mujoco/half_cheetah_jump/half_cheetah_jump.py

@@ -1,9 +1,9 @@
 import os
-from typing import Tuple, Union, Optional
+from typing import Tuple, Union, Optional, Any, Dict
 
 import numpy as np
-from gym.core import ObsType
+from gymnasium.core import ObsType
-from gym.envs.mujoco.half_cheetah_v4 import HalfCheetahEnv
+from gymnasium.envs.mujoco.half_cheetah_v4 import HalfCheetahEnv
 
 MAX_EPISODE_STEPS_HALFCHEETAHJUMP = 100
 
@@ -44,7 +44,8 @@ class HalfCheetahJumpEnv(HalfCheetahEnv):
         ## Didnt use fell_over, because base env also has no done condition - Paul and Marc
         # fell_over = abs(self.sim.data.qpos[2]) > 2.5  # how to figure out if the cheetah fell over? -> 2.5 oke?
         # TODO: Should a fall over be checked here?
-        done = False
+        terminated = False
+        truncated = False
 
         ctrl_cost = self.control_cost(action)
         costs = ctrl_cost
@@ -63,17 +64,17 @@ class HalfCheetahJumpEnv(HalfCheetahEnv):
             'max_height': self.max_height
         }
 
-        return observation, reward, done, info
+        return observation, reward, terminated, truncated, info
 
     def _get_obs(self):
         return np.append(super()._get_obs(), self.goal)
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False,
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
-              options: Optional[dict] = None, ) -> Union[ObsType, Tuple[ObsType, dict]]:
+            -> Tuple[ObsType, Dict[str, Any]]:
         self.max_height = 0
         self.current_step = 0
         self.goal = self.np_random.uniform(1.1, 1.6, 1)  # 1.1 1.6
-        return super().reset()
+        return super().reset(seed=seed, options=options)
 
     # overwrite reset_model to make it deterministic
     def reset_model(self):

fancy_gym/envs/mujoco/hopper_jump/hopper_jump.py

@@ -1,7 +1,7 @@
 import os
 
 import numpy as np
-from gym.envs.mujoco.hopper_v4 import HopperEnv
+from gymnasium.envs.mujoco.hopper_v4 import HopperEnv
 
 MAX_EPISODE_STEPS_HOPPERJUMP = 250
 
@@ -88,7 +88,8 @@ class HopperJumpEnv(HopperEnv):
 
         ctrl_cost = self.control_cost(action)
         costs = ctrl_cost
-        done = False
+        terminated = False
+        truncated = False
 
         goal_dist = np.linalg.norm(site_pos_after - self.goal)
         if self.contact_dist is None and self.contact_with_floor:
@@ -115,7 +116,7 @@ class HopperJumpEnv(HopperEnv):
             healthy=self.is_healthy,
             contact_dist=self.contact_dist or 0
         )
-        return observation, reward, done, info
+        return observation, reward, terminated, truncated, info
 
     def _get_obs(self):
         # goal_dist = self.data.get_site_xpos('foot_site') - self.goal

fancy_gym/envs/mujoco/hopper_jump/hopper_jump_on_box.py

@@ -1,7 +1,9 @@
 import os
+from typing import Optional, Dict, Any, Tuple
 
 import numpy as np
-from gym.envs.mujoco.hopper_v4 import HopperEnv
+from gymnasium.core import ObsType
+from gymnasium.envs.mujoco.hopper_v4 import HopperEnv
 
 MAX_EPISODE_STEPS_HOPPERJUMPONBOX = 250
 
@@ -74,10 +76,10 @@ class HopperJumpOnBoxEnv(HopperEnv):
 
         costs = ctrl_cost
 
-        done = fell_over or self.hopper_on_box
+        terminated = fell_over or self.hopper_on_box
 
-        if self.current_step >= self.max_episode_steps or done:
+        if self.current_step >= self.max_episode_steps or terminated:
-            done = False
+            done = False  # TODO why are we doing this???
 
             max_height = self.max_height.copy()
             min_distance = self.min_distance.copy()
@@ -122,12 +124,13 @@ class HopperJumpOnBoxEnv(HopperEnv):
             'goal': self.box_x,
         }
 
-        return observation, reward, done, info
+        return observation, reward, terminated, info
 
     def _get_obs(self):
         return np.append(super()._get_obs(), self.box_x)
 
-    def reset(self):
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
+            -> Tuple[ObsType, Dict[str, Any]]:
 
         self.max_height = 0
         self.min_distance = 5000
@@ -136,7 +139,7 @@ class HopperJumpOnBoxEnv(HopperEnv):
         if self.context:
             self.box_x = self.np_random.uniform(1, 3, 1)
             self.model.body("box").pos = [self.box_x[0], 0, 0]
-        return super().reset()
+        return super().reset(seed=seed, options=options)
 
     # overwrite reset_model to make it deterministic
     def reset_model(self):
@@ -151,20 +154,5 @@ class HopperJumpOnBoxEnv(HopperEnv):
         observation = self._get_obs()
         return observation
 
-if __name__ == '__main__':
-    render_mode = "human"  # "human" or "partial" or "final"
-    env = HopperJumpOnBoxEnv()
-    obs = env.reset()
 
-    for i in range(2000):
-        # objective.load_result("/tmp/cma")
-        # test with random actions
-        ac = env.action_space.sample()
-        obs, rew, d, info = env.step(ac)
-        if i % 10 == 0:
-            env.render(mode=render_mode)
-        if d:
-            print('After ', i, ' steps, done: ', d)
-            env.reset()
 
-    env.close()

fancy_gym/envs/mujoco/hopper_throw/hopper_throw.py

@@ -1,8 +1,9 @@
 import os
-from typing import Optional
+from typing import Optional, Any, Dict, Tuple
 
 import numpy as np
-from gym.envs.mujoco.hopper_v4 import HopperEnv
+from gymnasium.core import ObsType
+from gymnasium.envs.mujoco.hopper_v4 import HopperEnv
 
 MAX_EPISODE_STEPS_HOPPERTHROW = 250
 
@@ -56,14 +57,14 @@ class HopperThrowEnv(HopperEnv):
 
         # done = self.done TODO We should use this, not sure why there is no other termination; ball_landed should be enough, because we only look at the throw itself? - Paul and Marc
         ball_landed = bool(self.get_body_com("ball")[2] <= 0.05)
-        done = ball_landed
+        terminated = ball_landed
 
         ctrl_cost = self.control_cost(action)
         costs = ctrl_cost
 
         rewards = 0
 
-        if self.current_step >= self.max_episode_steps or done:
+        if self.current_step >= self.max_episode_steps or terminated:
             distance_reward = -np.linalg.norm(ball_pos_after - self.goal) if self.context else \
                 self._forward_reward_weight * ball_pos_after
             healthy_reward = 0 if self.context else self.healthy_reward * self.current_step
@@ -78,16 +79,18 @@ class HopperThrowEnv(HopperEnv):
             '_steps': self.current_step,
             'goal': self.goal,
         }
+        truncated = False
 
-        return observation, reward, done, info
+        return observation, reward, terminated, truncated, info
 
     def _get_obs(self):
         return np.append(super()._get_obs(), self.goal)
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None):
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
+            -> Tuple[ObsType, Dict[str, Any]]:
         self.current_step = 0
         self.goal = self.goal = self.np_random.uniform(2.0, 6.0, 1)  # 0.5 8.0
-        return super().reset()
+        return super().reset(seed=seed, options=options)
 
     # overwrite reset_model to make it deterministic
     def reset_model(self):
@@ -103,20 +106,3 @@ class HopperThrowEnv(HopperEnv):
         return observation
 
 
-if __name__ == '__main__':
-    render_mode = "human"  # "human" or "partial" or "final"
-    env = HopperThrowEnv()
-    obs = env.reset()
-
-    for i in range(2000):
-        # objective.load_result("/tmp/cma")
-        # test with random actions
-        ac = env.action_space.sample()
-        obs, rew, d, info = env.step(ac)
-        if i % 10 == 0:
-            env.render(mode=render_mode)
-        if d:
-            print('After ', i, ' steps, done: ', d)
-            env.reset()
-
-    env.close()

fancy_gym/envs/mujoco/hopper_throw/hopper_throw_in_basket.py

@@ -1,8 +1,9 @@
 import os
-from typing import Optional
+from typing import Optional, Any, Dict, Tuple
 
 import numpy as np
-from gym.envs.mujoco.hopper_v4 import HopperEnv
+from gymnasium.envs.mujoco.hopper_v4 import HopperEnv
+from gymnasium.core import ObsType
 
 MAX_EPISODE_STEPS_HOPPERTHROWINBASKET = 250
 
@@ -72,7 +73,7 @@ class HopperThrowInBasketEnv(HopperEnv):
             self.ball_in_basket = True
 
         ball_landed = self.get_body_com("ball")[2] <= 0.05
-        done = bool(ball_landed or is_in_basket)
+        terminated = bool(ball_landed or is_in_basket)
 
         rewards = 0
 
@@ -80,7 +81,7 @@ class HopperThrowInBasketEnv(HopperEnv):
 
         costs = ctrl_cost
 
-        if self.current_step >= self.max_episode_steps or done:
+        if self.current_step >= self.max_episode_steps or terminated:
 
             if is_in_basket:
                 if not self.context:
@@ -101,13 +102,16 @@ class HopperThrowInBasketEnv(HopperEnv):
         info = {
             'ball_pos': ball_pos[0],
         }
+        truncated = False
 
-        return observation, reward, done, info
+        return observation, reward, terminated, truncated, info
 
     def _get_obs(self):
         return np.append(super()._get_obs(), self.basket_x)
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None):
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
+            -> Tuple[ObsType, Dict[str, Any]]:
+
         if self.max_episode_steps == 10:
             # We have to initialize this here, because the spec is only added after creating the env.
             self.max_episode_steps = self.spec.max_episode_steps
@@ -117,7 +121,7 @@ class HopperThrowInBasketEnv(HopperEnv):
         if self.context:
             self.basket_x = self.np_random.uniform(low=3, high=7, size=1)
             self.model.body("basket_ground").pos[:] = [self.basket_x[0], 0, 0]
-        return super().reset()
+        return super().reset(seed=seed, options=options)
 
     # overwrite reset_model to make it deterministic
     def reset_model(self):
@@ -134,20 +138,4 @@ class HopperThrowInBasketEnv(HopperEnv):
         return observation
 
 
-if __name__ == '__main__':
-    render_mode = "human"  # "human" or "partial" or "final"
-    env = HopperThrowInBasketEnv()
-    obs = env.reset()
 
-    for i in range(2000):
-        # objective.load_result("/tmp/cma")
-        # test with random actions
-        ac = env.action_space.sample()
-        obs, rew, d, info = env.step(ac)
-        if i % 10 == 0:
-            env.render(mode=render_mode)
-        if d:
-            print('After ', i, ' steps, done: ', d)
-            env.reset()
-
-    env.close()

fancy_gym/envs/mujoco/reacher/reacher.py

@@ -1,9 +1,9 @@
 import os
 
 import numpy as np
-from gym import utils
+from gymnasium import utils
-from gym.envs.mujoco import MujocoEnv
+from gymnasium.envs.mujoco import MujocoEnv
-from gym.spaces import Box
+from gymnasium.spaces import Box
 
 MAX_EPISODE_STEPS_REACHER = 200
 

fancy_gym/envs/mujoco/walker_2d_jump/walker_2d_jump.py

@@ -1,8 +1,9 @@
 import os
-from typing import Optional
+from typing import Optional, Any, Dict, Tuple
 
 import numpy as np
-from gym.envs.mujoco.walker2d_v4 import Walker2dEnv
+from gymnasium.envs.mujoco.walker2d_v4 import Walker2dEnv
+from gymnasium.core import ObsType
 
 MAX_EPISODE_STEPS_WALKERJUMP = 300
 
@@ -54,13 +55,13 @@ class Walker2dJumpEnv(Walker2dEnv):
 
         self.max_height = max(height, self.max_height)
 
-        done = bool(height < 0.2)
+        terminated = bool(height < 0.2)
 
         ctrl_cost = self.control_cost(action)
         costs = ctrl_cost
         rewards = 0
-        if self.current_step >= self.max_episode_steps or done:
+        if self.current_step >= self.max_episode_steps or terminated:
-            done = True
+            terminated = True
             height_goal_distance = -10 * (np.linalg.norm(self.max_height - self.goal))
             healthy_reward = self.healthy_reward * self.current_step
 
@@ -73,17 +74,19 @@ class Walker2dJumpEnv(Walker2dEnv):
             'max_height': self.max_height,
             'goal': self.goal,
         }
+        truncated = False
 
-        return observation, reward, done, info
+        return observation, reward, terminated, truncated, info
 
     def _get_obs(self):
         return np.append(super()._get_obs(), self.goal)
 
-    def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None):
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
+            -> Tuple[ObsType, Dict[str, Any]]:
         self.current_step = 0
         self.max_height = 0
         self.goal = self.np_random.uniform(1.5, 2.5, 1)  # 1.5 3.0
-        return super().reset()
+        return super().reset(seed=seed, options=options)
 
     # overwrite reset_model to make it deterministic
     def reset_model(self):
@@ -98,20 +101,3 @@ class Walker2dJumpEnv(Walker2dEnv):
         observation = self._get_obs()
         return observation
 
-
-if __name__ == '__main__':
-    render_mode = "human"  # "human" or "partial" or "final"
-    env = Walker2dJumpEnv()
-    obs = env.reset()
-
-    for i in range(6000):
-        # test with random actions
-        ac = env.action_space.sample()
-        obs, rew, d, info = env.step(ac)
-        if i % 10 == 0:
-            env.render(mode=render_mode)
-        if d:
-            print('After ', i, ' steps, done: ', d)
-            env.reset()
-
-    env.close()

fancy_gym/meta/__init__.py

@@ -1,6 +1,6 @@
 from copy import deepcopy
 
-from gym import register
+from gymnasium import register
 
 from . import goal_object_change_mp_wrapper, goal_change_mp_wrapper, goal_endeffector_change_mp_wrapper, \
     object_change_mp_wrapper

fancy_gym/open_ai/__init__.py

@@ -1,6 +1,6 @@
 from copy import deepcopy
 
-from gym import register
+from gymnasium import register
 
 from . import mujoco
 from .deprecated_needs_gym_robotics import robotics

fancy_gym/utils/make_env_helpers.py

@@ -1,18 +1,17 @@
 import logging
-import re
 import uuid
 from collections.abc import MutableMapping
 from copy import deepcopy
 from math import ceil
 from typing import Iterable, Type, Union
 
-import gym
+import gymnasium as gym
 import numpy as np
-from gym.envs.registration import register, registry
+from gymnasium.envs.registration import register, registry
-from gym.utils import seeding
 
 try:
     from dm_control import suite, manipulation
+    from shimmy.dm_control_compatibility import EnvType
 except ImportError:
     pass
 
@@ -83,15 +82,20 @@ def make(env_id: str, seed: int, **kwargs):
     if framework == 'metaworld':
         # MetaWorld environment
         env = make_metaworld(env_id, seed, **kwargs)
-    elif framework == 'dmc':
+    # elif framework == 'dmc':
-        # DeepMind Control environment
+    # Deprecated: With shimmy gym now has native support for deepmind envs
-        env = make_dmc(env_id, seed, **kwargs)
+    #     # DeepMind Control environment
+    #     env = make_dmc(env_id, seed, **kwargs)
     else:
         env = make_gym(env_id, seed, **kwargs)
 
-    np_random, _ = seeding.np_random(seed)
+    # try:
-    env.np_random = np_random
+    env.reset(seed=seed)
-    # env.seed(seed)
+    # except TypeError:
+    #     # Support for older gym envs that do not have seeding
+    #     # env.seed(seed)
+    #     np_random, _ = seeding.np_random(seed)
+    #     env.np_random = np_random
     env.action_space.seed(seed)
     env.observation_space.seed(seed)
 
@@ -161,7 +165,7 @@ def make_bb(
     traj_gen_kwargs['action_dim'] = traj_gen_kwargs.get('action_dim', np.prod(env.action_space.shape).item())
 
     if black_box_kwargs.get('duration') is None:
-        black_box_kwargs['duration'] = env.spec.max_episode_steps * env.dt
+        black_box_kwargs['duration'] = get_env_duration(env)
     if phase_kwargs.get('tau') is None:
         phase_kwargs['tau'] = black_box_kwargs['duration']
 
@@ -180,6 +184,24 @@ def make_bb(
     return bb_env
 
 
+def get_env_duration(env: gym.Env):
+    try:
+        # TODO Remove if this is in the compatibility class
+        duration = env.spec.max_episode_steps * env.dt
+    except (AttributeError, TypeError) as e:
+        logging.error(f'Attributes env.spec.max_episode_steps and env.dt are not available. '
+                      f'Assuming you are using dm_control. Please make sure you have ran '
+                      f'"pip install shimmy[dm_control]" for that.')
+        if env.env_type is EnvType.COMPOSER:
+            max_episode_steps = ceil(env.unwrapped._time_limit / env.dt)
+        elif env.env_type is EnvType.RL_CONTROL:
+            max_episode_steps = int(env.unwrapped._step_limit)
+        else:
+            raise e
+        duration = max_episode_steps * env.control_timestep()
+    return duration
+
+
 def make_bb_env_helper(**kwargs):
     """
     Helper function for registering a black box gym environment.
@@ -229,52 +251,53 @@ def make_bb_env_helper(**kwargs):
                    basis_kwargs=basis_kwargs, **kwargs, seed=seed)
 
 
-def make_dmc(
+# Deprecated: With shimmy gym now has native support for deepmind envs
-        env_id: str,
+# def make_dmc(
-        seed: int = None,
+#         env_id: str,
-        visualize_reward: bool = True,
+#         seed: int = None,
-        time_limit: Union[None, float] = None,
+#         visualize_reward: bool = True,
-        **kwargs
+#         time_limit: Union[None, float] = None,
-):
+#         **kwargs
-    if not re.match(r"\w+-\w+", env_id):
+# ):
-        raise ValueError("env_id does not have the following structure: 'domain_name-task_name'")
+#     if not re.match(r"\w+-\w+", env_id):
-    domain_name, task_name = env_id.split("-")
+#         raise ValueError("env_id does not have the following structure: 'domain_name-task_name'")
-
+#     domain_name, task_name = env_id.split("-")
-    if task_name.endswith("_vision"):
+#
-        # TODO
+#     if task_name.endswith("_vision"):
-        raise ValueError("The vision interface for manipulation tasks is currently not supported.")
+#         # TODO
-
+#         raise ValueError("The vision interface for manipulation tasks is currently not supported.")
-    if (domain_name, task_name) not in suite.ALL_TASKS and task_name not in manipulation.ALL:
+#
-        raise ValueError(f'Specified domain "{domain_name}" and task "{task_name}" combination does not exist.')
+#     if (domain_name, task_name) not in suite.ALL_TASKS and task_name not in manipulation.ALL:
-
+#         raise ValueError(f'Specified domain "{domain_name}" and task "{task_name}" combination does not exist.')
-    # env_id = f'dmc_{domain_name}_{task_name}_{seed}-v1'
+#
-    gym_id = uuid.uuid4().hex + '-v1'
+#     # env_id = f'dmc_{domain_name}_{task_name}_{seed}-v1'
-
+#     gym_id = uuid.uuid4().hex + '-v1'
-    task_kwargs = {'random': seed}
+#
-    if time_limit is not None:
+#     task_kwargs = {'random': seed}
-        task_kwargs['time_limit'] = time_limit
+#     if time_limit is not None:
-
+#         task_kwargs['time_limit'] = time_limit
-    # create task
+#
-    # Accessing private attribute because DMC does not expose time_limit or step_limit.
+#     # create task
-    # Only the current time_step/time as well as the control_timestep can be accessed.
+#     # Accessing private attribute because DMC does not expose time_limit or step_limit.
-    if domain_name == "manipulation":
+#     # Only the current time_step/time as well as the control_timestep can be accessed.
-        env = manipulation.load(environment_name=task_name, seed=seed)
+#     if domain_name == "manipulation":
-        max_episode_steps = ceil(env._time_limit / env.control_timestep())
+#         env = manipulation.load(environment_name=task_name, seed=seed)
-    else:
+#         max_episode_steps = ceil(env._time_limit / env.control_timestep())
-        env = suite.load(domain_name=domain_name, task_name=task_name, task_kwargs=task_kwargs,
+#     else:
-                         visualize_reward=visualize_reward, environment_kwargs=kwargs)
+#         env = suite.load(domain_name=domain_name, task_name=task_name, task_kwargs=task_kwargs,
-        max_episode_steps = int(env._step_limit)
+#                          visualize_reward=visualize_reward, environment_kwargs=kwargs)
-
+#         max_episode_steps = int(env._step_limit)
-    register(
+#
-        id=gym_id,
+#     register(
-        entry_point='fancy_gym.dmc.dmc_wrapper:DMCWrapper',
+#         id=gym_id,
-        kwargs={'env': lambda: env},
+#         entry_point='fancy_gym.dmc.dmc_wrapper:DMCWrapper',
-        max_episode_steps=max_episode_steps,
+#         kwargs={'env': lambda: env},
-    )
+#         max_episode_steps=max_episode_steps,
-
+#     )
-    env = gym.make(gym_id)
+#
-    env.seed(seed)
+#     env = gym.make(gym_id)
-    return env
+#     env.seed(seed)
+#     return env
 
 
 def make_metaworld(env_id: str, seed: int, **kwargs):
@@ -288,12 +311,17 @@ def make_metaworld(env_id: str, seed: int, **kwargs):
     # New argument to use global seeding
     _env.seeded_rand_vec = True
 
+    max_episode_steps = _env.max_path_length
+
+    # TODO remove this as soon as there is support for the new API
+    _env = gym.wrappers.EnvCompatibility(_env)
+
     gym_id = uuid.uuid4().hex + '-v1'
 
     register(
         id=gym_id,
         entry_point=lambda: _env,
-        max_episode_steps=_env.max_path_length,
+        max_episode_steps=max_episode_steps,
     )
 
     # TODO enable checker when the incorrect dtype of obs and observation space are fixed by metaworld

fancy_gym/utils/time_aware_observation.py

@@ -1,45 +1,11 @@
-"""
+import gymnasium as gym
-Adapted from: https://github.com/openai/gym/blob/907b1b20dd9ac0cba5803225059b9c6673702467/gym/wrappers/time_aware_observation.py
-License: MIT
-Copyright (c) 2016 OpenAI (https://openai.com)
-
-Wrapper for adding time aware observations to environment observation.
-"""
-import gym
 import numpy as np
-from gym.spaces import Box
 
 
-class TimeAwareObservation(gym.ObservationWrapper):
+class TimeAwareObservation(gym.wrappers.TimeAwareObservation):
-    """Augment the observation with the current time step in the episode.
-
-    The observation space of the wrapped environment is assumed to be a flat :class:`Box`.
-    In particular, pixel observations are not supported. This wrapper will append the current timestep
-     within the current episode to the observation.
-
-    Example:
-        >>> import gym
-        >>> env = gym.make('CartPole-v1')
-        >>> env = TimeAwareObservation(env)
-        >>> env.reset()
-        array([ 0.03810719,  0.03522411,  0.02231044, -0.01088205,  0.        ])
-        >>> env.step(env.action_space.sample())[0]
-        array([ 0.03881167, -0.16021058,  0.0220928 ,  0.28875574,  1.        ])
-    """
 
     def __init__(self, env: gym.Env):
-        """Initialize :class:`TimeAwareObservation` that requires an environment with a flat :class:`Box`
-        observation space.
-
-        Args:
-            env: The environment to apply the wrapper
-        """
         super().__init__(env)
-        assert isinstance(env.observation_space, Box)
-        low = np.append(self.observation_space.low, 0.0)
-        high = np.append(self.observation_space.high, 1.0)
-        self.observation_space = Box(low, high, dtype=self.observation_space.dtype)
-        self.t = 0
         self._max_episode_steps = env.spec.max_episode_steps
 
     def observation(self, observation):
@@ -52,27 +18,3 @@ class TimeAwareObservation(gym.ObservationWrapper):
             The observation with the time step appended to
         """
         return np.append(observation, self.t / self._max_episode_steps)
-
-    def step(self, action):
-        """Steps through the environment, incrementing the time step.
-
-        Args:
-            action: The action to take
-
-        Returns:
-            The environment's step using the action.
-        """
-        self.t += 1
-        return super().step(action)
-
-    def reset(self, **kwargs):
-        """Reset the environment setting the time to zero.
-
-        Args:
-            **kwargs: Kwargs to apply to env.reset()
-
-        Returns:
-            The reset environment
-        """
-        self.t = 0
-        return super().reset(**kwargs)