Replace all links to temporary docs hosting with final ones

.github/workflows/host_docs_static.yml

@@ -1,40 +0,0 @@
-name: Deploy static docs to Pages
-
-on:
-  push:
-    branches: ["release"]
-
-  # Allows you to run this workflow manually from the Actions tab
-  workflow_dispatch:
-
-# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
-permissions:
-  contents: read
-  pages: write
-  id-token: write
-
-# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
-# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
-concurrency:
-  group: "pages"
-  cancel-in-progress: false
-
-jobs:
-  # Single deploy job since we're just deploying
-  deploy:
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Setup Pages
-        uses: actions/configure-pages@v4
-      - name: Upload artifact
-        uses: actions/upload-pages-artifact@v3
-        with:
-          path: 'docs/build/html'
-      - name: Deploy to GitHub Pages
-        id: deployment
-        uses: actions/deploy-pages@v4

README.md

@@ -10,25 +10,25 @@ Built upon the foundation of [Gymnasium](https://gymnasium.farama.org) (a mainta
 
 **Key Features**:
 
-- **New Challenging Environments**: `fancy_gym` includes several new environments ([Panda Box Pushing](https://dominik-roth.eu/fancy/envs/fancy/mujoco.html#box-pushing), [Table Tennis](https://dominik-roth.eu/fancy/envs/fancy/mujoco.html#table-tennis), [etc.](https://dominik-roth.eu/fancy/envs/fancy/index.html)) that present a higher degree of difficulty, pushing the boundaries of reinforcement learning research.
+- **New Challenging Environments**: `fancy_gym` includes several new environments ([Panda Box Pushing](https://alrhub.github.io/fancy_gym/envs/fancy/mujoco.html#box-pushing), [Table Tennis](https://alrhub.github.io/fancy_gym/envs/fancy/mujoco.html#table-tennis), [etc.](https://alrhub.github.io/fancy_gym/envs/fancy/index.html)) that present a higher degree of difficulty, pushing the boundaries of reinforcement learning research.
 - **Support for Movement Primitives**: `fancy_gym` supports a range of movement primitives (MPs), including Dynamic Movement Primitives (DMPs), Probabilistic Movement Primitives (ProMP), and Probabilistic Dynamic Movement Primitives (ProDMP).
 - **Upgrade to Movement Primitives**: With our framework, it’s straightforward to transform standard Gymnasium environments into environments that support movement primitives.
-- **Benchmark Suite Compatibility**: `fancy_gym` makes it easy to access renowned benchmark suites such as [DeepMind Control](dominik-roth.eu/fancy/envs/dmc.html)
-  and [Metaworld](https://dominik-roth.eu/fancy/envs/meta.html), whether you want to use them in the regular step-based setting or using MPs.
-- **Contribute Your Own Environments**: If you’re inspired to create custom gym environments, both step-based and with movement primitives, this [guide](https://dominik-roth.eu/fancy/guide/upgrading_envs.html) will assist you. We encourage and highly appreciate submissions via PRs to integrate these environments into `fancy_gym`.
+- **Benchmark Suite Compatibility**: `fancy_gym` makes it easy to access renowned benchmark suites such as [DeepMind Control](https://alrhub.github.io/fancy_gym/envs/dmc.html)
+  and [Metaworld](https://alrhub.github.io/fancy_gym/envs/meta.html), whether you want to use them in the regular step-based setting or using MPs.
+- **Contribute Your Own Environments**: If you’re inspired to create custom gym environments, both step-based and with movement primitives, this [guide](https://alrhub.github.io/fancy_gym/guide/upgrading_envs.html) will assist you. We encourage and highly appreciate submissions via PRs to integrate these environments into `fancy_gym`.
 
 ## Quickstart Guide
 
 | ⚠ We recommend installing `fancy_gym` into a virtual environment as provided by [venv](https://docs.python.org/3/library/venv.html), [Poetry](https://python-poetry.org/) or [Conda](https://docs.conda.io/en/latest/). |
 | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
 
-Install via pip [or use an alternative installation method](https://dominik-roth.eu/fancy/guide/installation.html)
+Install via pip [or use an alternative installation method](https://alrhub.github.io/fancy_gym/guide/installation.html)
 
 ```bash
     pip install 'fancy_gym[all]'
 ```
 
-Try out one of our step-based environments [or explore our other envs](https://dominik-roth.eu/fancy/envs/fancy/index.html)
+Try out one of our step-based environments [or explore our other envs](https://alrhub.github.io/fancy_gym/envs/fancy/index.html)
 
 ```python
    import gymnasium as gym
@@ -48,7 +48,7 @@ Try out one of our step-based environments [or explore our other envs](https://d
             observation, info = env.reset()
 ```
 
-Explore the MP-based variant [or learn more about Movement Primitives (MPs)](https://dominik-roth.eu/fancy/guide/episodic_rl.html)
+Explore the MP-based variant [or learn more about Movement Primitives (MPs)](https://alrhub.github.io/fancy_gym/guide/episodic_rl.html)
 
 ```python
    import gymnasium as gym
@@ -66,7 +66,7 @@ Explore the MP-based variant [or learn more about Movement Primitives (MPs)](htt
 
 ## Documentation
 
-Documentation for `fancy_gym` can be found [here](https://dominik-roth.eu/fancy); Usage Examples can be found [here](https://dominik-roth.eu/fancy/examples/general.html).
+Documentation for `fancy_gym` can be found [here](https://alrhub.github.io/fancy_gym/); Usage Examples can be found [here](https://alrhub.github.io/fancy_gym/examples/general.html).
 
 ## Citing the Project
 

fancy_gym/envs/mujoco/ant_jump/ant_jump.py

@@ -115,7 +115,6 @@ class AntJumpEnv(AntEnvCustomXML):
                          contact_force_range=contact_force_range,
                          reset_noise_scale=reset_noise_scale,
                          exclude_current_positions_from_observation=exclude_current_positions_from_observation, **kwargs)
-        self.render_active = False
 
     def step(self, action):
         self.current_step += 1
@@ -154,15 +153,8 @@ class AntJumpEnv(AntEnvCustomXML):
         }
         truncated = False
 
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return obs, reward, terminated, truncated, info
 
-    def render(self):
-        self.render_active = True
-        return super().render()
-
     def _get_obs(self):
         return np.append(super()._get_obs(), self.goal)
 

fancy_gym/envs/mujoco/beerpong/beerpong.py

@@ -44,7 +44,6 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
     }
 
     def __init__(self, **kwargs):
-        utils.EzPickle.__init__(self)
         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")
@@ -90,7 +89,7 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
             observation_space=self.observation_space,
             **kwargs
         )
-        self.render_active = False
+        utils.EzPickle.__init__(self)
 
     @property
     def start_pos(self):
@@ -170,15 +169,8 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
 
         truncated = False
 
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return ob, reward, terminated, truncated, infos
 
-    def render(self):
-        self.render_active = True
-        return super().render()
-
     def _get_obs(self):
         theta = self.data.qpos.flat[:7].copy()
         theta_dot = self.data.qvel.flat[:7].copy()

fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py

@@ -4,7 +4,6 @@ import numpy as np
 from gymnasium import utils, spaces
 from gymnasium.envs.mujoco import MujocoEnv
 from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import rot_to_quat, get_quaternion_error, rotation_distance
-from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import rot_to_quat, get_quaternion_error, rotation_distance
 from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import q_max, q_min, q_dot_max, q_torque_max
 from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import desired_rod_quat
 
@@ -61,7 +60,6 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
                            frame_skip=self.frame_skip,
                            observation_space=self.observation_space, **kwargs)
         self.action_space = spaces.Box(low=-1, high=1, shape=(7,))
-        self.render_active = False
 
     def step(self, action):
         action = 10 * np.clip(action, self.action_space.low, self.action_space.high)
@@ -110,15 +108,8 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
         terminated = episode_end and infos['is_success']
         truncated = episode_end and not infos['is_success']
 
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return obs, reward, terminated, truncated, infos
 
-    def render(self):
-        self.render_active = True
-        return super().render()
-
     def reset_model(self):
         # rest box to initial position
         self.set_state(self.init_qpos_box_pushing, self.init_qvel_box_pushing)

fancy_gym/envs/mujoco/half_cheetah_jump/half_cheetah_jump.py

@@ -60,11 +60,7 @@ class HalfCheetahEnvCustomXML(HalfCheetahEnv):
             default_camera_config=DEFAULT_CAMERA_CONFIG,
             **kwargs,
         )
-        self.render_active = False
 
-    def render(self):
-        self.render_active = True
-        return super().render()
 
 class HalfCheetahJumpEnv(HalfCheetahEnvCustomXML):
     """
@@ -124,9 +120,6 @@ class HalfCheetahJumpEnv(HalfCheetahEnvCustomXML):
             'max_height': self.max_height
         }
 
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return observation, reward, terminated, truncated, info
 
     def _get_obs(self):

fancy_gym/envs/mujoco/hopper_jump/hopper_jump.py

@@ -88,12 +88,6 @@ class HopperEnvCustomXML(HopperEnv):
             **kwargs,
         )
 
-        self.render_active = False
-
-    def render(self):
-        self.render_active = True
-        return super().render()
-
 
 class HopperJumpEnv(HopperEnvCustomXML):
     """
@@ -207,10 +201,6 @@ class HopperJumpEnv(HopperEnvCustomXML):
             healthy=self.is_healthy,
             contact_dist=self.contact_dist or 0
         )
-
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return observation, reward, terminated, truncated, info
 
     def _get_obs(self):

fancy_gym/envs/mujoco/hopper_jump/hopper_jump_on_box.py

@@ -140,9 +140,6 @@ class HopperJumpOnBoxEnv(HopperEnvCustomXML):
 
         truncated = self.current_step >= self.max_episode_steps and not terminated
 
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return observation, reward, terminated, truncated, info
 
     def _get_obs(self):

fancy_gym/envs/mujoco/hopper_throw/hopper_throw.py

@@ -61,8 +61,6 @@ class HopperThrowEnv(HopperEnvCustomXML):
                          exclude_current_positions_from_observation=exclude_current_positions_from_observation,
                          **kwargs)
 
-        self.render_active = False
-
     def step(self, action):
         self.current_step += 1
         self.do_simulation(action, self.frame_skip)
@@ -96,15 +94,8 @@ class HopperThrowEnv(HopperEnvCustomXML):
         }
         truncated = False
 
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return observation, reward, terminated, truncated, info
 
-    def render(self):
-        self.render_active = True
-        return super().render()
-
     def _get_obs(self):
         return np.append(super()._get_obs(), self.goal)
 

fancy_gym/envs/mujoco/hopper_throw/hopper_throw_in_basket.py

@@ -68,7 +68,6 @@ class HopperThrowInBasketEnv(HopperEnvCustomXML):
                          reset_noise_scale=reset_noise_scale,
                          exclude_current_positions_from_observation=exclude_current_positions_from_observation,
                          **kwargs)
-        self.render_active = False
 
     def step(self, action):
 
@@ -119,15 +118,8 @@ class HopperThrowInBasketEnv(HopperEnvCustomXML):
         }
         truncated = False
 
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return observation, reward, terminated, truncated, info
 
-    def render(self):
-        self.render_active = True
-        return super().render()
-
     def _get_obs(self):
         return np.append(super()._get_obs(), self.basket_x)
 

fancy_gym/envs/mujoco/reacher/reacher.py

@@ -47,8 +47,6 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
                            **kwargs
                            )
 
-        self.render_active = False
-
     def step(self, action):
         self._steps += 1
 
@@ -79,15 +77,8 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
             goal=self.goal if hasattr(self, "goal") else None
         )
 
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return ob, reward, terminated, truncated, info
 
-    def render(self):
-        self.render_active = True
-        return super().render()
-
     def distance_reward(self):
         vec = self.get_body_com("fingertip") - self.get_body_com("target")
         return -self._reward_weight * np.linalg.norm(vec)

fancy_gym/envs/mujoco/table_tennis/table_tennis_env.py

@@ -71,8 +71,6 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
                            observation_space=self.observation_space,
                            **kwargs)
 
-        self.render_active = False
-
         if ctxt_dim == 2:
             self.context_bounds = CONTEXT_BOUNDS_2DIMS
         elif ctxt_dim == 4:
@@ -160,15 +158,8 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
 
         terminated, truncated = self._terminated, False
 
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return self._get_obs(), reward, terminated, truncated, info
 
-    def render(self):
-        self.render_active = True
-        return super().render()
-
     def _contact_checker(self, id_1, id_2):
         for coni in range(0, self.data.ncon):
             con = self.data.contact[coni]

fancy_gym/envs/mujoco/walker_2d_jump/walker_2d_jump.py

@@ -79,8 +79,6 @@ class Walker2dEnvCustomXML(Walker2dEnv):
             **kwargs,
         )
 
-        self.render_active = False
-
 
 class Walker2dJumpEnv(Walker2dEnvCustomXML):
     """
@@ -147,15 +145,8 @@ class Walker2dJumpEnv(Walker2dEnvCustomXML):
         }
         truncated = False
 
-        if self.render_active and self.render_mode=='human':
-            self.render()
-
         return observation, reward, terminated, truncated, info
 
-    def render(self):
-        self.render_active = True
-        return super().render()
-
     def _get_obs(self):
         return np.append(super()._get_obs(), self.goal)
 

fancy_gym/examples/example_replanning_envs.py

@@ -3,14 +3,14 @@ import fancy_gym
 
 
 def example_run_replanning_env(env_name="fancy_ProDMP/BoxPushingDenseReplan-v0", seed=1, iterations=1, render=False):
-    env = gym.make(env_name, render_mode='human' if render else None)
+    env = gym.make(env_name)
     env.reset(seed=seed)
     for i in range(iterations):
         while True:
             ac = env.action_space.sample()
             obs, reward, terminated, truncated, info = env.step(ac)
             if render:
-                env.render()
+                env.render(mode="human")
             if terminated or truncated:
                 env.reset()
                 break
@@ -38,13 +38,13 @@ def example_custom_replanning_envs(seed=0, iteration=100, render=True):
                         'replanning_schedule': lambda pos, vel, obs, action, t: t % 25 == 0,
                         'condition_on_desired': True}
 
-    base_env = gym.make(base_env_id, render_mode='human' if render else None)
+    base_env = gym.make(base_env_id)
     env = fancy_gym.make_bb(env=base_env, wrappers=wrappers, black_box_kwargs=black_box_kwargs,
                             traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs,
                             phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs,
                             seed=seed)
     if render:
-        env.render()
+        env.render(mode="human")
 
     obs = env.reset()
 

fancy_gym/examples/examples_dmc.py

@@ -17,7 +17,7 @@ def example_dmc(env_id="dm_control/fish-swim", seed=1, iterations=1000, render=T
     Returns:
 
     """
-    env = gym.make(env_id, render_mode='human' if render else None)
+    env = gym.make(env_id)
     rewards = 0
     obs = env.reset(seed=seed)
     print("observation shape:", env.observation_space.shape)
@@ -26,7 +26,7 @@ def example_dmc(env_id="dm_control/fish-swim", seed=1, iterations=1000, render=T
     for i in range(iterations):
         ac = env.action_space.sample()
         if render:
-            env.render()
+            env.render(mode="human")
         obs, reward, terminated, truncated, info = env.step(ac)
         rewards += reward
 
@@ -84,7 +84,7 @@ def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
     # basis_generator_kwargs = {'basis_generator_type': 'rbf',
     #                           'num_basis': 5
     #                           }
-    base_env = gym.make(base_env_id, render_mode='human' if render else None)
+    base_env = gym.make(base_env_id)
     env = fancy_gym.make_bb(env=base_env, wrappers=wrappers, black_box_kwargs={},
                             traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs,
                             phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs,
@@ -96,7 +96,7 @@ def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
     # It is also possible to change them mode multiple times when
     # e.g. only every nth trajectory should be displayed.
     if render:
-        env.render()
+        env.render(mode="human")
 
     rewards = 0
     obs = env.reset()
@@ -115,7 +115,7 @@ def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
     env.close()
     del env
 
-def main(render = False):
+def main(render = True):
     # # Standard DMC Suite tasks
     example_dmc("dm_control/fish-swim", seed=10, iterations=1000, render=render)
     #

fancy_gym/examples/examples_general.py

@@ -21,7 +21,7 @@ def example_general(env_id="Pendulum-v1", seed=1, iterations=1000, render=True):
 
     """
 
-    env = gym.make(env_id, render_mode='human' if render else None)
+    env = gym.make(env_id)
     rewards = 0
     obs = env.reset(seed=seed)
     print("Observation shape: ", env.observation_space.shape)
@@ -85,7 +85,7 @@ def example_async(env_id="fancy/HoleReacher-v0", n_cpu=4, seed=int('533D', 16),
     # do not return values above threshold
     return *map(lambda v: np.stack(v)[:n_samples], buffer.values()),
 
-def main(render = False):
+def main(render = True):
     # Basic gym task
     example_general("Pendulum-v1", seed=10, iterations=200, render=render)
 

fancy_gym/examples/examples_metaworld.py

@@ -2,7 +2,7 @@ import gymnasium as gym
 import fancy_gym
 
 
-def example_meta(env_id="metaworld/button-press-v2", seed=1, iterations=1000, render=True):
+def example_meta(env_id="fish-swim", seed=1, iterations=1000, render=True):
     """
     Example for running a MetaWorld based env in the step based setting.
     The env_id has to be specified as `task_name-v2`. V1 versions are not supported and we always
@@ -18,7 +18,7 @@ def example_meta(env_id="metaworld/button-press-v2", seed=1, iterations=1000, re
     Returns:
 
     """
-    env = gym.make(env_id, render_mode='human' if render else None)
+    env = gym.make(env_id)
     rewards = 0
     obs = env.reset(seed=seed)
     print("observation shape:", env.observation_space.shape)
@@ -27,7 +27,9 @@ def example_meta(env_id="metaworld/button-press-v2", seed=1, iterations=1000, re
     for i in range(iterations):
         ac = env.action_space.sample()
         if render:
-            env.render()
+            # THIS NEEDS TO BE SET TO FALSE FOR NOW, BECAUSE THE INTERFACE FOR RENDERING IS DIFFERENT TO BASIC GYM
+            # TODO: Remove this, when Metaworld fixes its interface.
+            env.render(False)
         obs, reward, terminated, truncated, info = env.step(ac)
         rewards += reward
         if terminated or truncated:
@@ -79,7 +81,7 @@ def example_custom_meta_and_mp(seed=1, iterations=1, render=True):
     basis_generator_kwargs = {'basis_generator_type': 'rbf',
                               'num_basis': 5
                               }
-    base_env = gym.make(base_env_id, render_mode='human' if render else None)
+    base_env = gym.make(base_env_id)
     env = fancy_gym.make_bb(env=base_env, wrappers=wrappers, black_box_kwargs={},
                             traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs,
                             phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs,
@@ -91,7 +93,7 @@ def example_custom_meta_and_mp(seed=1, iterations=1, render=True):
     # It is also possible to change them mode multiple times when
     # e.g. only every nth trajectory should be displayed.
     if render:
-        env.render()
+        env.render(mode="human")
 
     rewards = 0
     obs = env.reset(seed=seed)

fancy_gym/examples/examples_open_ai.py

@@ -13,13 +13,15 @@ def example_mp(env_name, seed=1, render=True):
     Returns:
 
     """
-    env = gym.make(env_name, render_mode='human' if render else None)
+    env = gym.make(env_name)
 
     returns = 0
     obs = env.reset(seed=seed)
     # number of samples/full trajectories (multiple environment steps)
     for i in range(10):
         if render and i % 2 == 0:
+            env.render(mode="human")
+        else:
             env.render()
         ac = env.action_space.sample()
         obs, reward, terminated, truncated, info = env.step(ac)