Also install metaworld during workflow run

.github/workflows/host_docs_static.yml

@@ -0,0 +1,40 @@
+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

.github/workflows/pytest.yml

@@ -0,0 +1,32 @@
+name: Run pytest
+
+on:
+  push:
+    branches:
+#      - master
+      - release
+  pull_request:
+    branches:
+#      - master
+      - release
+
+jobs:
+  pytest:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+      
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.x'
+
+      - name: Install package dependencies
+        run: pip install .[testing]
+
+      - name: Install Metaworld
+        run: pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld
+
+      - name: Run pytest
+        run: pytest

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://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.
+- **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.
 - **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](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`.
+- **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`.
 
 ## 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://alrhub.github.io/fancy_gym/guide/installation.html)
+Install via pip [or use an alternative installation method](https://dominik-roth.eu/fancy/guide/installation.html)
 
 ```bash
     pip install 'fancy_gym[all]'
 ```
 
-Try out one of our step-based environments [or explore our other envs](https://alrhub.github.io/fancy_gym/envs/fancy/index.html)
+Try out one of our step-based environments [or explore our other envs](https://dominik-roth.eu/fancy/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://a
             observation, info = env.reset()
 ```
 
-Explore the MP-based variant [or learn more about Movement Primitives (MPs)](https://alrhub.github.io/fancy_gym/guide/episodic_rl.html)
+Explore the MP-based variant [or learn more about Movement Primitives (MPs)](https://dominik-roth.eu/fancy/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://alrhub.github.io/fancy_gym/); Usage Examples can be found [here](https://alrhub.github.io/fancy_gym/examples/general.html).
+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).
 
 ## Citing the Project
 

test/test_all_gym_builtin_envs.py

@@ -21,15 +21,21 @@ GYM_MP_IDS = fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS['all']
 SEED = 1
 
 
+known_fail_functionality = ['LunarLander-v2', 'Blackjack-v1', 'CliffWalking-v0']
 @pytest.mark.parametrize('env_id', GYM_IDS)
 def test_step_gym_functionality(env_id: str):
     """Tests that step environments run without errors using random actions."""
+    if env_id in known_fail_functionality:
+        pytest.xfail(f"{env_id} is expected to fail the functionality test")
     run_env(env_id)
 
 
+known_fail_deteminism = ['LunarLanderContinuous-v2', 'CliffWalking-v0']
 @pytest.mark.parametrize('env_id', GYM_IDS)
 def test_step_gym_determinism(env_id: str):
     """Tests that for step environments identical seeds produce identical trajectories."""
+    if env_id in known_fail_deteminism:
+        pytest.xfail(f"{env_id} is expected to fail the determinism test")
     run_env_determinism(env_id, SEED)
 
 

test/test_dmc_envs.py

@@ -15,15 +15,21 @@ DMC_MP_IDS = fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS['all']
 SEED = 1
 
 
+known_fail_functionality = []
 @pytest.mark.parametrize('env_id', DMC_IDS)
 def test_step_dm_control_functionality(env_id: str):
     """Tests that suite step environments run without errors using random actions."""
+    if env_id in known_fail_functionality:
+        pytest.xfail(f"{env_id} is expected to fail the functionality test")
     run_env(env_id, 5000, wrappers=[gym.wrappers.FlattenObservation])
 
 
+known_fail_deteminism = ['dm_control/CmuHumanoidMazeForage-v0', 'dm_control/CmuHumanoidHeterogeneousForage-v0', 'dm_control/RodentMazeForage-v0', 'dm_control/RodentTwoTouch-v0']
 @pytest.mark.parametrize('env_id', DMC_IDS)
 def test_step_dm_control_determinism(env_id: str):
     """Tests that for step environments identical seeds produce identical trajectories."""
+    if env_id in known_fail_deteminism:
+        pytest.xfail(f"{env_id} is expected to fail the determinism test")
     run_env_determinism(env_id, SEED, 5000, wrappers=[gym.wrappers.FlattenObservation])