diff --git a/.github/workflows/ensure-release-tagged.yaml b/.github/workflows/ensure-release-tagged.yaml deleted file mode 100644 index e58e31e..0000000 --- a/.github/workflows/ensure-release-tagged.yaml +++ /dev/null @@ -1,26 +0,0 @@ -name: Ensure Tagged Commits on Release - -on: - pull_request: - branches: - - release - -jobs: - check_tag: - runs-on: ubuntu-latest - steps: - - name: Check out code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Check if base commit of PR is tagged - run: | - BASE_COMMIT=$(jq -r .pull_request.base.sha < "$GITHUB_EVENT_PATH") - TAG=$(git tag --contains $BASE_COMMIT) - if [ -z "$TAG" ]; then - echo "Base commit of PR is not tagged. PRs onto release must be tagged with the version number." - exit 1 - fi - echo "Base commit of PR is tagged. Check passed." - diff --git a/.github/workflows/ensure-version-consistency.yaml b/.github/workflows/ensure-version-consistency.yaml new file mode 100644 index 0000000..c3c85df --- /dev/null +++ b/.github/workflows/ensure-version-consistency.yaml @@ -0,0 +1,52 @@ +name: Ensure Version Consistency on PR to Release + +on: + pull_request: + branches: + - release + +jobs: + check_version_and_tag: + runs-on: ubuntu-latest + strategy: + fail-fast: true # Terminate the job immediately if any step fails + steps: + - name: Check out code + uses: actions/checkout@v4 + with: + fetch-depth: 0 # Necessary to fetch all tags for comparison + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.x' + + - name: Install dependencies + run: | + python -m pip install toml + + - name: Extract version from pyproject.toml + run: | + echo "Extracting version from pyproject.toml" + VERSION=$(python -c 'import toml; print(toml.load("pyproject.toml")["project"]["version"])') + echo "Version in pyproject.toml is $VERSION" + echo "VERSION=$VERSION" >> $GITHUB_ENV + + - name: Get tag for the PR's head commit + run: | + PR_HEAD_SHA=$(jq -r .pull_request.head.sha < "$GITHUB_EVENT_PATH") + TAG=$(git tag --contains $PR_HEAD_SHA) + echo "Tag on PR's head commit is $TAG" + echo "TAG=$TAG" >> $GITHUB_ENV + + - name: Compare version and tag + run: | + if [ -z "$TAG" ]; then + echo "Head commit of PR is not tagged. Ensure the head commit of PRs onto release is tagged with the version number." + exit 1 + elif [ "$VERSION" != "$TAG" ]; then + echo "Version in pyproject.toml ($VERSION) does not match the git tag ($TAG)." + exit 1 + else + echo "Version and git tag match. Check passed." + fi diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index 1b5fa08..902cec5 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -8,6 +8,8 @@ on: jobs: publish: name: Publish to PyPI + strategy: + fail-fast: true # Terminate the job immediately if any step fails runs-on: ubuntu-latest steps: - name: Check out code @@ -15,19 +17,24 @@ jobs: with: fetch-depth: 0 # This fetches all history for all branches and tags - - name: Check if commit is tagged + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.x" + + - name: Validate version against tag run: | + VERSION=$(python -c 'import toml; print(toml.load("pyproject.toml")["project"]["version"])') TAG=$(git tag --contains HEAD) if [ -z "$TAG" ]; then echo "Commit is not tagged. Failing the workflow." exit 1 fi - echo "Commit is tagged. Proceeding with the workflow." - - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: "3.x" + if [ "$VERSION" != "$TAG" ]; then + echo "Version in pyproject.toml ($VERSION) does not match the git tag ($TAG). Failing the workflow." + exit 1 + fi + echo "Version and commit tag match. Proceeding with the workflow." - name: Install pypa/build/setuptools/twine run: >- @@ -36,9 +43,6 @@ jobs: build setuptools twine --user - - name: Prevent fallback onto setup.py - run: rm setup.py - - name: Build a binary wheel and a source tarball run: python3 -m build diff --git a/README.md b/README.md index af6d2d3..73dce66 100644 --- a/README.md +++ b/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 diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 24c61da..ae5c3a7 100644 Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ diff --git a/docs/build/doctrees/examples/dmc.doctree b/docs/build/doctrees/examples/dmc.doctree index 958cf19..21fc3d1 100644 Binary files a/docs/build/doctrees/examples/dmc.doctree and b/docs/build/doctrees/examples/dmc.doctree differ diff --git a/docs/build/doctrees/examples/general.doctree b/docs/build/doctrees/examples/general.doctree index ffba9f8..8d0f367 100644 Binary files a/docs/build/doctrees/examples/general.doctree and b/docs/build/doctrees/examples/general.doctree differ diff --git a/docs/build/doctrees/examples/metaworld.doctree b/docs/build/doctrees/examples/metaworld.doctree index 481db99..7b82b77 100644 Binary files a/docs/build/doctrees/examples/metaworld.doctree and b/docs/build/doctrees/examples/metaworld.doctree differ diff --git a/docs/build/doctrees/examples/movement_primitives.doctree b/docs/build/doctrees/examples/movement_primitives.doctree index 5f08c93..c3dae92 100644 Binary files a/docs/build/doctrees/examples/movement_primitives.doctree and b/docs/build/doctrees/examples/movement_primitives.doctree differ diff --git a/docs/build/doctrees/examples/open_ai.doctree b/docs/build/doctrees/examples/open_ai.doctree index 303687b..c1321c3 100644 Binary files a/docs/build/doctrees/examples/open_ai.doctree and b/docs/build/doctrees/examples/open_ai.doctree differ diff --git a/docs/build/doctrees/examples/replanning_envs.doctree b/docs/build/doctrees/examples/replanning_envs.doctree index 75a3f46..5a6c386 100644 Binary files a/docs/build/doctrees/examples/replanning_envs.doctree and b/docs/build/doctrees/examples/replanning_envs.doctree differ diff --git a/docs/build/doctrees/guide/installation.doctree b/docs/build/doctrees/guide/installation.doctree index d1366cd..fa389f2 100644 Binary files a/docs/build/doctrees/guide/installation.doctree and b/docs/build/doctrees/guide/installation.doctree differ diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo index 9b8f8aa..b5dcb4b 100644 --- a/docs/build/html/.buildinfo +++ b/docs/build/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 28ec069496fc0ad05c8b9641549626a6 +config: 36919d67c12a677d3f16f60d980b0313 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_modules/fancy_gym/envs/registry.html b/docs/build/html/_modules/fancy_gym/envs/registry.html index 2955545..bdf26be 100644 --- a/docs/build/html/_modules/fancy_gym/envs/registry.html +++ b/docs/build/html/_modules/fancy_gym/envs/registry.html @@ -3,7 +3,7 @@ - fancy_gym.envs.registry — Fancy Gym 0.2 documentation + fancy_gym.envs.registry — Fancy Gym 0.3.0 documentation @@ -38,7 +38,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html index 0bd248e..e7cfe06 100644 --- a/docs/build/html/_modules/index.html +++ b/docs/build/html/_modules/index.html @@ -3,7 +3,7 @@ - Overview: module code — Fancy Gym 0.2 documentation + Overview: module code — Fancy Gym 0.3.0 documentation @@ -38,7 +38,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/_sources/guide/installation.rst.txt b/docs/build/html/_sources/guide/installation.rst.txt index 0077ba4..1c10973 100644 --- a/docs/build/html/_sources/guide/installation.rst.txt +++ b/docs/build/html/_sources/guide/installation.rst.txt @@ -32,7 +32,7 @@ since they are not avaible on PyPI yet. Install metaworld via .. code:: bash - pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld + pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@c822f28f582ba1ad49eb5dcf61016566f28003ba#egg=metaworld Installation from master ~~~~~~~~~~~~~~~~~~~~~~~~ @@ -70,4 +70,4 @@ Metaworld has to be installed manually with .. code:: bash - pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld \ No newline at end of file + pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@c822f28f582ba1ad49eb5dcf61016566f28003ba#egg=metaworld diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js index 4c7b1ee..44e1488 100644 --- a/docs/build/html/_static/documentation_options.js +++ b/docs/build/html/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '0.2', + VERSION: '0.3.0', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/build/html/api.html b/docs/build/html/api.html index 3545a26..262f31a 100644 --- a/docs/build/html/api.html +++ b/docs/build/html/api.html @@ -4,7 +4,7 @@ - API — Fancy Gym 0.2 documentation + API — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/envs/dmc.html b/docs/build/html/envs/dmc.html index 851815c..522efd8 100644 --- a/docs/build/html/envs/dmc.html +++ b/docs/build/html/envs/dmc.html @@ -4,7 +4,7 @@ - DeepMind Control (DMC) — Fancy Gym 0.2 documentation + DeepMind Control (DMC) — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/envs/fancy/airhockey.html b/docs/build/html/envs/fancy/airhockey.html index 27629cf..3fcdc19 100644 --- a/docs/build/html/envs/fancy/airhockey.html +++ b/docs/build/html/envs/fancy/airhockey.html @@ -4,7 +4,7 @@ - AirHockey — Fancy Gym 0.2 documentation + AirHockey — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/envs/fancy/classic_control.html b/docs/build/html/envs/fancy/classic_control.html index 09b2c23..d9d5a40 100644 --- a/docs/build/html/envs/fancy/classic_control.html +++ b/docs/build/html/envs/fancy/classic_control.html @@ -4,7 +4,7 @@ - Classic Control — Fancy Gym 0.2 documentation + Classic Control — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/envs/fancy/index.html b/docs/build/html/envs/fancy/index.html index 0091290..8b1418a 100644 --- a/docs/build/html/envs/fancy/index.html +++ b/docs/build/html/envs/fancy/index.html @@ -4,7 +4,7 @@ - Fancy — Fancy Gym 0.2 documentation + Fancy — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/envs/fancy/mujoco.html b/docs/build/html/envs/fancy/mujoco.html index e132bec..11d2273 100644 --- a/docs/build/html/envs/fancy/mujoco.html +++ b/docs/build/html/envs/fancy/mujoco.html @@ -4,7 +4,7 @@ - Mujoco — Fancy Gym 0.2 documentation + Mujoco — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/envs/meta.html b/docs/build/html/envs/meta.html index 1dc1d49..ffde63f 100644 --- a/docs/build/html/envs/meta.html +++ b/docs/build/html/envs/meta.html @@ -4,7 +4,7 @@ - Metaworld — Fancy Gym 0.2 documentation + Metaworld — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/envs/open_ai.html b/docs/build/html/envs/open_ai.html index 296a946..4182d6d 100644 --- a/docs/build/html/envs/open_ai.html +++ b/docs/build/html/envs/open_ai.html @@ -4,7 +4,7 @@ - Gymnasium — Fancy Gym 0.2 documentation + Gymnasium — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/examples/dmc.html b/docs/build/html/examples/dmc.html index 81c9feb..57ef9a1 100644 --- a/docs/build/html/examples/dmc.html +++ b/docs/build/html/examples/dmc.html @@ -4,7 +4,7 @@ - DeepMind Control Examples — Fancy Gym 0.2 documentation + DeepMind Control Examples — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
@@ -126,7 +126,7 @@ 17 Returns: 18 19 """ - 20 env = gym.make(env_id) + 20 env = gym.make(env_id, render_mode='human' if render else None) 21 rewards = 0 22 obs = env.reset(seed=seed) 23 print("observation shape:", env.observation_space.shape) @@ -135,7 +135,7 @@ 26 for i in range(iterations): 27 ac = env.action_space.sample() 28 if render: - 29 env.render(mode="human") + 29 env.render() 30 obs, reward, terminated, truncated, info = env.step(ac) 31 rewards += reward 32 @@ -193,58 +193,68 @@ 84 # basis_generator_kwargs = {'basis_generator_type': 'rbf', 85 # 'num_basis': 5 86 # } - 87 env = fancy_gym.make_bb(env_id=base_env_id, wrappers=wrappers, black_box_kwargs={}, - 88 traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs, - 89 phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs, - 90 seed=seed) - 91 - 92 # This renders the full MP trajectory - 93 # It is only required to call render() once in the beginning, which renders every consecutive trajectory. - 94 # Resetting to no rendering, can be achieved by render(mode=None). - 95 # It is also possible to change them mode multiple times when - 96 # e.g. only every nth trajectory should be displayed. - 97 if render: - 98 env.render(mode="human") - 99 -100 rewards = 0 -101 obs = env.reset() -102 -103 # number of samples/full trajectories (multiple environment steps) -104 for i in range(iterations): -105 ac = env.action_space.sample() -106 obs, reward, terminated, truncated, info = env.step(ac) -107 rewards += reward -108 -109 if terminated or truncated: -110 print(base_env_id, rewards) -111 rewards = 0 -112 obs = env.reset() -113 -114 env.close() -115 del env -116 + 87 base_env = gym.make(base_env_id, render_mode='human' if render else None) + 88 env = fancy_gym.make_bb(env=base_env, wrappers=wrappers, black_box_kwargs={}, + 89 traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs, + 90 phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs, + 91 seed=seed) + 92 + 93 # This renders the full MP trajectory + 94 # It is only required to call render() once in the beginning, which renders every consecutive trajectory. + 95 # Resetting to no rendering, can be achieved by render(mode=None). + 96 # It is also possible to change them mode multiple times when + 97 # e.g. only every nth trajectory should be displayed. + 98 if render: + 99 env.render() +100 +101 rewards = 0 +102 obs = env.reset() +103 +104 # number of samples/full trajectories (multiple environment steps) +105 for i in range(iterations): +106 ac = env.action_space.sample() +107 obs, reward, terminated, truncated, info = env.step(ac) +108 rewards += reward +109 +110 if terminated or truncated: +111 print(base_env_id, rewards) +112 rewards = 0 +113 obs = env.reset() +114 +115 env.close() +116 del env 117 -118if __name__ == '__main__': -119 # Disclaimer: DMC environments require the seed to be specified in the beginning. -120 # Adjusting it afterwards with env.seed() is not recommended as it does not affect the underlying physics. -121 -122 # For rendering DMC -123 # export MUJOCO_GL="osmesa" -124 render = True -125 -126 # # Standard DMC Suite tasks -127 example_dmc("dm_control/fish-swim", seed=10, iterations=1000, render=render) -128 # -129 # # Manipulation tasks -130 # # Disclaimer: The vision versions are currently not integrated and yield an error -131 example_dmc("dm_control/manipulation-reach_site_features", seed=10, iterations=250, render=render) -132 # -133 # # Gym + DMC hybrid task provided in the MP framework -134 example_dmc("dm_control_ProMP/ball_in_cup-catch-v0", seed=10, iterations=1, render=render) -135 -136 # Custom DMC task # Different seed, because the episode is longer for this example and the name+seed combo is -137 # already registered above -138 example_custom_dmc_and_mp(seed=11, iterations=1, render=render) +118def main(render = False): +119 # # Standard DMC Suite tasks +120 example_dmc("dm_control/fish-swim", seed=10, iterations=1000, render=render) +121 # +122 # # Manipulation tasks +123 # # Disclaimer: The vision versions are currently not integrated and yield an error +124 example_dmc("dm_control/reach_site_features", seed=10, iterations=250, render=render) +125 # +126 # # Gym + DMC hybrid task provided in the MP framework +127 example_dmc("dm_control_ProMP/ball_in_cup-catch-v0", seed=10, iterations=1, render=render) +128 +129 # Custom DMC task # Different seed, because the episode is longer for this example and the name+seed combo is +130 # already registered above +131 example_custom_dmc_and_mp(seed=11, iterations=1, render=render) +132 +133 # # Standard DMC Suite tasks +134 example_dmc("dm_control/fish-swim", seed=10, iterations=1000, render=render) +135 # +136 # # Manipulation tasks +137 # # Disclaimer: The vision versions are currently not integrated and yield an error +138 example_dmc("dm_control/reach_site_features", seed=10, iterations=250, render=render) +139 # +140 # # Gym + DMC hybrid task provided in the MP framework +141 example_dmc("dm_control_ProMP/ball_in_cup-catch-v0", seed=10, iterations=1, render=render) +142 +143 # Custom DMC task # Different seed, because the episode is longer for this example and the name+seed combo is +144 # already registered above +145 example_custom_dmc_and_mp(seed=11, iterations=1, render=render) +146 +147if __name__ == '__main__': +148 main()
diff --git a/docs/build/html/examples/general.html b/docs/build/html/examples/general.html index 11bd623..31a6318 100644 --- a/docs/build/html/examples/general.html +++ b/docs/build/html/examples/general.html @@ -4,7 +4,7 @@ - General Usage Examples — Fancy Gym 0.2 documentation + General Usage Examples — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
@@ -130,7 +130,7 @@ 21 22 """ 23 - 24 env = gym.make(env_id) + 24 env = gym.make(env_id, render_mode='human' if render else None) 25 rewards = 0 26 obs = env.reset(seed=seed) 27 print("Observation shape: ", env.observation_space.shape) @@ -194,21 +194,21 @@ 85 # do not return values above threshold 86 return *map(lambda v: np.stack(v)[:n_samples], buffer.values()), 87 - 88 - 89if __name__ == '__main__': - 90 render = True + 88def main(render = False): + 89 # Basic gym task + 90 example_general("Pendulum-v1", seed=10, iterations=200, render=render) 91 - 92 # Basic gym task - 93 example_general("Pendulum-v1", seed=10, iterations=200, render=render) + 92 # Mujoco task from framework + 93 example_general("fancy/Reacher5d-v0", seed=10, iterations=200, render=render) 94 - 95 # Mujoco task from framework - 96 example_general("fancy/Reacher5d-v0", seed=10, iterations=200, render=render) + 95 # # OpenAI Mujoco task + 96 example_general("HalfCheetah-v2", seed=10, render=render) 97 - 98 # # OpenAI Mujoco task - 99 example_general("HalfCheetah-v2", seed=10, render=render) + 98 # Vectorized multiprocessing environments + 99 # example_async(env_id="HoleReacher-v0", n_cpu=2, seed=int('533D', 16), n_samples=2 * 200) 100 -101 # Vectorized multiprocessing environments -102 # example_async(env_id="HoleReacher-v0", n_cpu=2, seed=int('533D', 16), n_samples=2 * 200) +101if __name__ == '__main__': +102 main()
diff --git a/docs/build/html/examples/metaworld.html b/docs/build/html/examples/metaworld.html index 7045b53..ff026d7 100644 --- a/docs/build/html/examples/metaworld.html +++ b/docs/build/html/examples/metaworld.html @@ -4,7 +4,7 @@ - Metaworld Examples — Fancy Gym 0.2 documentation + Metaworld Examples — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
@@ -111,7 +111,7 @@ 2import fancy_gym 3 4 - 5def example_meta(env_id="fish-swim", seed=1, iterations=1000, render=True): + 5def example_meta(env_id="metaworld/button-press-v2", seed=1, iterations=1000, render=True): 6 """ 7 Example for running a MetaWorld based env in the step based setting. 8 The env_id has to be specified as `task_name-v2`. V1 versions are not supported and we always @@ -127,7 +127,7 @@ 18 Returns: 19 20 """ - 21 env = gym.make(env_id) + 21 env = gym.make(env_id, render_mode='human' if render else None) 22 rewards = 0 23 obs = env.reset(seed=seed) 24 print("observation shape:", env.observation_space.shape) @@ -136,111 +136,104 @@ 27 for i in range(iterations): 28 ac = env.action_space.sample() 29 if render: - 30 # THIS NEEDS TO BE SET TO FALSE FOR NOW, BECAUSE THE INTERFACE FOR RENDERING IS DIFFERENT TO BASIC GYM - 31 # TODO: Remove this, when Metaworld fixes its interface. - 32 env.render(False) - 33 obs, reward, terminated, truncated, info = env.step(ac) - 34 rewards += reward - 35 if terminated or truncated: - 36 print(env_id, rewards) - 37 rewards = 0 - 38 obs = env.reset() - 39 - 40 env.close() - 41 del env - 42 - 43 - 44def example_custom_meta_and_mp(seed=1, iterations=1, render=True): - 45 """ - 46 Example for running a custom movement primitive based environments. - 47 Our already registered environments follow the same structure. - 48 Hence, this also allows to adjust hyperparameters of the movement primitives. - 49 Yet, we recommend the method above if you are just interested in chaining those parameters for existing tasks. - 50 We appreciate PRs for custom environments (especially MP wrappers of existing tasks) - 51 for our repo: https://github.com/ALRhub/fancy_gym/ - 52 Args: - 53 seed: seed for deterministic behaviour (TODO: currently not working due to an issue in MetaWorld code) - 54 iterations: Number of rollout steps to run - 55 render: Render the episode (TODO: currently not working due to an issue in MetaWorld code) + 30 env.render() + 31 obs, reward, terminated, truncated, info = env.step(ac) + 32 rewards += reward + 33 if terminated or truncated: + 34 print(env_id, rewards) + 35 rewards = 0 + 36 obs = env.reset(seed=seed+i+1) + 37 + 38 env.close() + 39 del env + 40 + 41 + 42def example_custom_meta_and_mp(seed=1, iterations=1, render=True): + 43 """ + 44 Example for running a custom movement primitive based environments. + 45 Our already registered environments follow the same structure. + 46 Hence, this also allows to adjust hyperparameters of the movement primitives. + 47 Yet, we recommend the method above if you are just interested in chaining those parameters for existing tasks. + 48 We appreciate PRs for custom environments (especially MP wrappers of existing tasks) + 49 for our repo: https://github.com/ALRhub/fancy_gym/ + 50 Args: + 51 seed: seed for deterministic behaviour (TODO: currently not working due to an issue in MetaWorld code) + 52 iterations: Number of rollout steps to run + 53 render: Render the episode (TODO: currently not working due to an issue in MetaWorld code) + 54 + 55 Returns: 56 - 57 Returns: + 57 """ 58 - 59 """ - 60 - 61 # Base MetaWorld name, according to structure of above example - 62 base_env_id = "metaworld/button-press-v2" - 63 - 64 # Replace this wrapper with the custom wrapper for your environment by inheriting from the RawInterfaceWrapper. - 65 # You can also add other gym.Wrappers in case they are needed. - 66 wrappers = [fancy_gym.meta.goal_object_change_mp_wrapper.MPWrapper] - 67 # # For a ProMP - 68 # trajectory_generator_kwargs = {'trajectory_generator_type': 'promp'} - 69 # phase_generator_kwargs = {'phase_generator_type': 'linear'} - 70 # controller_kwargs = {'controller_type': 'metaworld'} - 71 # basis_generator_kwargs = {'basis_generator_type': 'zero_rbf', - 72 # 'num_basis': 5, - 73 # 'num_basis_zero_start': 1 - 74 # } - 75 - 76 # For a DMP - 77 trajectory_generator_kwargs = {'trajectory_generator_type': 'dmp'} - 78 phase_generator_kwargs = {'phase_generator_type': 'exp', - 79 'alpha_phase': 2} - 80 controller_kwargs = {'controller_type': 'metaworld'} - 81 basis_generator_kwargs = {'basis_generator_type': 'rbf', - 82 'num_basis': 5 - 83 } - 84 env = fancy_gym.make_bb(env_id=base_env_id, wrappers=wrappers, black_box_kwargs={}, - 85 traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs, - 86 phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs, - 87 seed=seed) - 88 - 89 # This renders the full MP trajectory - 90 # It is only required to call render() once in the beginning, which renders every consecutive trajectory. - 91 # Resetting to no rendering, can be achieved by render(mode=None). - 92 # It is also possible to change them mode multiple times when - 93 # e.g. only every nth trajectory should be displayed. - 94 if render: - 95 raise ValueError("Metaworld render interface bug does not allow to render() fixes its interface. " - 96 "A temporary workaround is to alter their code in MujocoEnv render() from " - 97 "`if not offscreen` to `if not offscreen or offscreen == 'human'`.") - 98 # TODO: Remove this, when Metaworld fixes its interface. - 99 # env.render(mode="human") -100 -101 rewards = 0 -102 obs = env.reset() -103 -104 # number of samples/full trajectories (multiple environment steps) -105 for i in range(iterations): -106 ac = env.action_space.sample() -107 obs, reward, terminated, truncated, info = env.step(ac) -108 rewards += reward + 59 # Base MetaWorld name, according to structure of above example + 60 base_env_id = "metaworld/button-press-v2" + 61 + 62 # Replace this wrapper with the custom wrapper for your environment by inheriting from the RawInterfaceWrapper. + 63 # You can also add other gym.Wrappers in case they are needed. + 64 wrappers = [fancy_gym.meta.goal_object_change_mp_wrapper.MPWrapper] + 65 # # For a ProMP + 66 # trajectory_generator_kwargs = {'trajectory_generator_type': 'promp'} + 67 # phase_generator_kwargs = {'phase_generator_type': 'linear'} + 68 # controller_kwargs = {'controller_type': 'metaworld'} + 69 # basis_generator_kwargs = {'basis_generator_type': 'zero_rbf', + 70 # 'num_basis': 5, + 71 # 'num_basis_zero_start': 1 + 72 # } + 73 + 74 # For a DMP + 75 trajectory_generator_kwargs = {'trajectory_generator_type': 'dmp'} + 76 phase_generator_kwargs = {'phase_generator_type': 'exp', + 77 'alpha_phase': 2} + 78 controller_kwargs = {'controller_type': 'metaworld'} + 79 basis_generator_kwargs = {'basis_generator_type': 'rbf', + 80 'num_basis': 5 + 81 } + 82 base_env = gym.make(base_env_id, render_mode='human' if render else None) + 83 env = fancy_gym.make_bb(env=base_env, wrappers=wrappers, black_box_kwargs={}, + 84 traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs, + 85 phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs, + 86 seed=seed) + 87 + 88 # This renders the full MP trajectory + 89 # It is only required to call render() once in the beginning, which renders every consecutive trajectory. + 90 # Resetting to no rendering, can be achieved by render(mode=None). + 91 # It is also possible to change them mode multiple times when + 92 # e.g. only every nth trajectory should be displayed. + 93 if render: + 94 env.render() + 95 + 96 rewards = 0 + 97 obs = env.reset(seed=seed) + 98 + 99 # number of samples/full trajectories (multiple environment steps) +100 for i in range(iterations): +101 ac = env.action_space.sample() +102 obs, reward, terminated, truncated, info = env.step(ac) +103 rewards += reward +104 +105 if terminated or truncated: +106 print(base_env_id, rewards) +107 rewards = 0 +108 obs = env.reset(seed=seed+i+1) 109 -110 if terminated or truncated: -111 print(base_env_id, rewards) -112 rewards = 0 -113 obs = env.reset() -114 -115 env.close() -116 del env -117 -118 -119if __name__ == '__main__': -120 # Disclaimer: MetaWorld environments require the seed to be specified in the beginning. -121 # Adjusting it afterwards with env.seed() is not recommended as it may not affect the underlying behavior. -122 -123 # For rendering it might be necessary to specify your OpenGL installation -124 # export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so -125 render = False -126 -127 # # Standard Meta world tasks -128 example_meta("metaworld/button-press-v2", seed=10, iterations=500, render=render) -129 -130 # # MP + MetaWorld hybrid task provided in the our framework -131 example_meta("metaworld_ProMP/ButtonPress-v2", seed=10, iterations=1, render=render) -132 # -133 # # Custom MetaWorld task -134 example_custom_meta_and_mp(seed=10, iterations=1, render=render) +110 env.close() +111 del env +112 +113def main(render = False): +114 # For rendering it might be necessary to specify your OpenGL installation +115 # export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so +116 +117 # # Standard Meta world tasks +118 example_meta("metaworld/button-press-v2", seed=10, iterations=500, render=render) +119 +120 # # MP + MetaWorld hybrid task provided in the our framework +121 example_meta("metaworld_ProMP/button-press-v2", seed=10, iterations=1, render=render) +122 # +123 # # Custom MetaWorld task +124 example_custom_meta_and_mp(seed=10, iterations=1, render=render) +125 +126if __name__ == '__main__': +127 main()
diff --git a/docs/build/html/examples/movement_primitives.html b/docs/build/html/examples/movement_primitives.html index 1943b1b..be0ce86 100644 --- a/docs/build/html/examples/movement_primitives.html +++ b/docs/build/html/examples/movement_primitives.html @@ -4,7 +4,7 @@ - Movement Primitives Examples — Fancy Gym 0.2 documentation + Movement Primitives Examples — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
@@ -135,252 +135,253 @@ 26 for i in range(iterations): 27 28 if render and i % 1 == 0: - 29 env.render() - 30 - 31 # Now the action space is not the raw action but the parametrization of the trajectory generator, - 32 # such as a ProMP - 33 ac = env.action_space.sample() - 34 # This executes a full trajectory and gives back the context (obs) of the last step in the trajectory, or the - 35 # full observation space of the last step, if replanning/sub-trajectory learning is used. The 'reward' is equal - 36 # to the return of a trajectory. Default is the sum over the step-wise rewards. - 37 obs, reward, terminated, truncated, info = env.step(ac) - 38 # Aggregated returns - 39 returns += reward - 40 - 41 if terminated or truncated: - 42 print(reward) - 43 obs = env.reset() - 44 env.close() - 45 - 46 - 47def example_custom_mp(env_name="fancy_ProMP/Reacher5d-v0", seed=1, iterations=1, render=True): - 48 """ - 49 Example for running a custom movement primitive based environments. - 50 Our already registered environments follow the same structure. - 51 Hence, this also allows to adjust hyperparameters of the movement primitives. - 52 Yet, we recommend the method above if you are just interested in changing those parameters for existing tasks. - 53 We appreciate PRs for custom environments (especially MP wrappers of existing tasks) - 54 for our repo: https://github.com/ALRhub/fancy_gym/ - 55 Args: - 56 seed: seed - 57 iterations: Number of rollout steps to run - 58 render: Render the episode - 59 - 60 Returns: + 29 # This renders the full MP trajectory + 30 # It is only required to call render() once in the beginning, which renders every consecutive trajectory. + 31 env.render() + 32 + 33 # Now the action space is not the raw action but the parametrization of the trajectory generator, + 34 # such as a ProMP + 35 ac = env.action_space.sample() + 36 # This executes a full trajectory and gives back the context (obs) of the last step in the trajectory, or the + 37 # full observation space of the last step, if replanning/sub-trajectory learning is used. The 'reward' is equal + 38 # to the return of a trajectory. Default is the sum over the step-wise rewards. + 39 obs, reward, terminated, truncated, info = env.step(ac) + 40 # Aggregated returns + 41 returns += reward + 42 + 43 if terminated or truncated: + 44 print(reward) + 45 obs = env.reset() + 46 env.close() + 47 + 48 + 49def example_custom_mp(env_name="fancy_ProMP/Reacher5d-v0", seed=1, iterations=1, render=True): + 50 """ + 51 Example for running a custom movement primitive based environments. + 52 Our already registered environments follow the same structure. + 53 Hence, this also allows to adjust hyperparameters of the movement primitives. + 54 Yet, we recommend the method above if you are just interested in changing those parameters for existing tasks. + 55 We appreciate PRs for custom environments (especially MP wrappers of existing tasks) + 56 for our repo: https://github.com/ALRhub/fancy_gym/ + 57 Args: + 58 seed: seed + 59 iterations: Number of rollout steps to run + 60 render: Render the episode 61 - 62 """ - 63 # Changing the arguments of the black box env is possible by providing them to gym through mp_config_override. - 64 # E.g. here for way to many basis functions - 65 env = gym.make(env_name, seed, mp_config_override={'basis_generator_kwargs': {'num_basis': 1000}}, render_mode='human' if render else None) - 66 - 67 returns = 0 - 68 obs = env.reset() - 69 - 70 # This time rendering every trajectory - 71 if render: - 72 env.render() - 73 - 74 # number of samples/full trajectories (multiple environment steps) - 75 for i in range(iterations): - 76 ac = env.action_space.sample() - 77 obs, reward, terminated, truncated, info = env.step(ac) - 78 returns += reward - 79 - 80 if terminated or truncated: - 81 print(i, reward) - 82 obs = env.reset() - 83 - 84 env.close() - 85 return obs - 86 - 87class Custom_MPWrapper(fancy_gym.envs.mujoco.reacher.MPWrapper): - 88 mp_config = { - 89 'ProMP': { - 90 'trajectory_generator_kwargs': { - 91 'trajectory_generator_type': 'promp', - 92 'weights_scale': 2 - 93 }, - 94 'phase_generator_kwargs': { - 95 'phase_generator_type': 'linear' - 96 }, - 97 'controller_kwargs': { - 98 'controller_type': 'velocity' - 99 }, -100 'basis_generator_kwargs': { -101 'basis_generator_type': 'zero_rbf', -102 'num_basis': 5, -103 'num_basis_zero_start': 1 -104 } -105 }, -106 'DMP': { -107 'trajectory_generator_kwargs': { -108 'trajectory_generator_type': 'dmp', -109 'weights_scale': 500 -110 }, -111 'phase_generator_kwargs': { -112 'phase_generator_type': 'exp', -113 'alpha_phase': 2.5 -114 }, -115 'controller_kwargs': { -116 'controller_type': 'velocity' -117 }, -118 'basis_generator_kwargs': { -119 'basis_generator_type': 'rbf', -120 'num_basis': 5 -121 } -122 } -123 } -124 -125 -126def example_fully_custom_mp(seed=1, iterations=1, render=True): -127 """ -128 Example for running a custom movement primitive based environments. -129 Our already registered environments follow the same structure. -130 Hence, this also allows to adjust hyperparameters of the movement primitives. -131 Yet, we recommend the method above if you are just interested in changing those parameters for existing tasks. -132 We appreciate PRs for custom environments (especially MP wrappers of existing tasks) -133 for our repo: https://github.com/ALRhub/fancy_gym/ -134 Args: -135 seed: seed -136 iterations: Number of rollout steps to run -137 render: Render the episode -138 -139 Returns: + 62 Returns: + 63 + 64 """ + 65 # Changing the arguments of the black box env is possible by providing them to gym through mp_config_override. + 66 # E.g. here for way to many basis functions + 67 env = gym.make(env_name, seed, mp_config_override={'basis_generator_kwargs': {'num_basis': 1000}}, render_mode='human' if render else None) + 68 + 69 returns = 0 + 70 obs = env.reset() + 71 + 72 # This time rendering every trajectory + 73 if render: + 74 env.render() + 75 + 76 # number of samples/full trajectories (multiple environment steps) + 77 for i in range(iterations): + 78 ac = env.action_space.sample() + 79 obs, reward, terminated, truncated, info = env.step(ac) + 80 returns += reward + 81 + 82 if terminated or truncated: + 83 print(i, reward) + 84 obs = env.reset() + 85 + 86 env.close() + 87 return obs + 88 + 89class Custom_MPWrapper(fancy_gym.envs.mujoco.reacher.MPWrapper): + 90 mp_config = { + 91 'ProMP': { + 92 'trajectory_generator_kwargs': { + 93 'trajectory_generator_type': 'promp', + 94 'weights_scale': 2 + 95 }, + 96 'phase_generator_kwargs': { + 97 'phase_generator_type': 'linear' + 98 }, + 99 'controller_kwargs': { +100 'controller_type': 'velocity' +101 }, +102 'basis_generator_kwargs': { +103 'basis_generator_type': 'zero_rbf', +104 'num_basis': 5, +105 'num_basis_zero_start': 1 +106 } +107 }, +108 'DMP': { +109 'trajectory_generator_kwargs': { +110 'trajectory_generator_type': 'dmp', +111 'weights_scale': 500 +112 }, +113 'phase_generator_kwargs': { +114 'phase_generator_type': 'exp', +115 'alpha_phase': 2.5 +116 }, +117 'controller_kwargs': { +118 'controller_type': 'velocity' +119 }, +120 'basis_generator_kwargs': { +121 'basis_generator_type': 'rbf', +122 'num_basis': 5 +123 } +124 } +125 } +126 +127 +128def example_fully_custom_mp(seed=1, iterations=1, render=True): +129 """ +130 Example for running a custom movement primitive based environments. +131 Our already registered environments follow the same structure. +132 Hence, this also allows to adjust hyperparameters of the movement primitives. +133 Yet, we recommend the method above if you are just interested in changing those parameters for existing tasks. +134 We appreciate PRs for custom environments (especially MP wrappers of existing tasks) +135 for our repo: https://github.com/ALRhub/fancy_gym/ +136 Args: +137 seed: seed +138 iterations: Number of rollout steps to run +139 render: Render the episode 140 -141 """ +141 Returns: 142 -143 base_env_id = "fancy/Reacher5d-v0" -144 custom_env_id = "fancy/Reacher5d-Custom-v0" -145 custom_env_id_DMP = "fancy_DMP/Reacher5d-Custom-v0" -146 custom_env_id_ProMP = "fancy_ProMP/Reacher5d-Custom-v0" -147 -148 fancy_gym.upgrade(custom_env_id, mp_wrapper=Custom_MPWrapper, add_mp_types=['ProMP', 'DMP'], base_id=base_env_id) +143 """ +144 +145 base_env_id = "fancy/Reacher5d-v0" +146 custom_env_id = "fancy/Reacher5d-Custom-v0" +147 custom_env_id_DMP = "fancy_DMP/Reacher5d-Custom-v0" +148 custom_env_id_ProMP = "fancy_ProMP/Reacher5d-Custom-v0" 149 -150 env = gym.make(custom_env_id_ProMP, render_mode='human' if render else None) +150 fancy_gym.upgrade(custom_env_id, mp_wrapper=Custom_MPWrapper, add_mp_types=['ProMP', 'DMP'], base_id=base_env_id) 151 -152 rewards = 0 -153 obs = env.reset() -154 -155 if render: -156 env.render() -157 -158 # number of samples/full trajectories (multiple environment steps) -159 for i in range(iterations): -160 ac = env.action_space.sample() -161 obs, reward, terminated, truncated, info = env.step(ac) -162 rewards += reward -163 -164 if terminated or truncated: -165 print(rewards) -166 rewards = 0 -167 obs = env.reset() -168 -169 try: # Some mujoco-based envs don't correlcty implement .close -170 env.close() -171 except: -172 pass -173 -174 -175def example_fully_custom_mp_alternative(seed=1, iterations=1, render=True): -176 """ -177 Instead of defining the mp_args in a new custom MP_Wrapper, they can also be provided during registration. -178 Args: -179 seed: seed -180 iterations: Number of rollout steps to run -181 render: Render the episode -182 -183 Returns: +152 env = gym.make(custom_env_id_ProMP, render_mode='human' if render else None) +153 +154 rewards = 0 +155 obs = env.reset() +156 +157 if render: +158 env.render() +159 +160 # number of samples/full trajectories (multiple environment steps) +161 for i in range(iterations): +162 ac = env.action_space.sample() +163 obs, reward, terminated, truncated, info = env.step(ac) +164 rewards += reward +165 +166 if terminated or truncated: +167 print(rewards) +168 rewards = 0 +169 obs = env.reset() +170 +171 try: # Some mujoco-based envs don't correlcty implement .close +172 env.close() +173 except: +174 pass +175 +176 +177def example_fully_custom_mp_alternative(seed=1, iterations=1, render=True): +178 """ +179 Instead of defining the mp_args in a new custom MP_Wrapper, they can also be provided during registration. +180 Args: +181 seed: seed +182 iterations: Number of rollout steps to run +183 render: Render the episode 184 -185 """ +185 Returns: 186 -187 base_env_id = "fancy/Reacher5d-v0" -188 custom_env_id = "fancy/Reacher5d-Custom-v0" -189 custom_env_id_ProMP = "fancy_ProMP/Reacher5d-Custom-v0" -190 -191 fancy_gym.upgrade(custom_env_id, mp_wrapper=fancy_gym.envs.mujoco.reacher.MPWrapper, add_mp_types=['ProMP'], base_id=base_env_id, mp_config_override= {'ProMP': { -192 'trajectory_generator_kwargs': { -193 'trajectory_generator_type': 'promp', -194 'weights_scale': 2 -195 }, -196 'phase_generator_kwargs': { -197 'phase_generator_type': 'linear' -198 }, -199 'controller_kwargs': { -200 'controller_type': 'velocity' -201 }, -202 'basis_generator_kwargs': { -203 'basis_generator_type': 'zero_rbf', -204 'num_basis': 5, -205 'num_basis_zero_start': 1 -206 } -207 }}) -208 -209 env = gym.make(custom_env_id_ProMP, render_mode='human' if render else None) +187 """ +188 +189 base_env_id = "fancy/Reacher5d-v0" +190 custom_env_id = "fancy/Reacher5d-Custom-v0" +191 custom_env_id_ProMP = "fancy_ProMP/Reacher5d-Custom-v0" +192 +193 fancy_gym.upgrade(custom_env_id, mp_wrapper=fancy_gym.envs.mujoco.reacher.MPWrapper, add_mp_types=['ProMP'], base_id=base_env_id, mp_config_override= {'ProMP': { +194 'trajectory_generator_kwargs': { +195 'trajectory_generator_type': 'promp', +196 'weights_scale': 2 +197 }, +198 'phase_generator_kwargs': { +199 'phase_generator_type': 'linear' +200 }, +201 'controller_kwargs': { +202 'controller_type': 'velocity' +203 }, +204 'basis_generator_kwargs': { +205 'basis_generator_type': 'zero_rbf', +206 'num_basis': 5, +207 'num_basis_zero_start': 1 +208 } +209 }}) 210 -211 rewards = 0 -212 obs = env.reset() -213 -214 if render: -215 env.render() -216 -217 # number of samples/full trajectories (multiple environment steps) -218 for i in range(iterations): -219 ac = env.action_space.sample() -220 obs, reward, terminated, truncated, info = env.step(ac) -221 rewards += reward -222 -223 if terminated or truncated: -224 print(rewards) -225 rewards = 0 -226 obs = env.reset() -227 -228 if render: -229 env.render() -230 -231 rewards = 0 -232 obs = env.reset() -233 -234 # number of samples/full trajectories (multiple environment steps) -235 for i in range(iterations): -236 ac = env.action_space.sample() -237 obs, reward, terminated, truncated, info = env.step(ac) -238 rewards += reward -239 -240 if terminated or truncated: -241 print(rewards) -242 rewards = 0 -243 obs = env.reset() -244 -245 try: # Some mujoco-based envs don't correlcty implement .close -246 env.close() -247 except: -248 pass -249 -250 -251def main(): -252 render = False -253 # DMP -254 example_mp("fancy_DMP/HoleReacher-v0", seed=10, iterations=5, render=render) -255 -256 # ProMP -257 example_mp("fancy_ProMP/HoleReacher-v0", seed=10, iterations=5, render=render) -258 example_mp("fancy_ProMP/BoxPushingTemporalSparse-v0", seed=10, iterations=1, render=render) -259 example_mp("fancy_ProMP/TableTennis4D-v0", seed=10, iterations=20, render=render) -260 -261 # ProDMP with Replanning -262 example_mp("fancy_ProDMP/BoxPushingDenseReplan-v0", seed=10, iterations=4, render=render) -263 example_mp("fancy_ProDMP/TableTennis4DReplan-v0", seed=10, iterations=20, render=render) -264 example_mp("fancy_ProDMP/TableTennisWindReplan-v0", seed=10, iterations=20, render=render) -265 -266 # Altered basis functions -267 obs1 = example_custom_mp("fancy_ProMP/Reacher5d-v0", seed=10, iterations=1, render=render) -268 -269 # Custom MP -270 example_fully_custom_mp(seed=10, iterations=1, render=render) -271 example_fully_custom_mp_alternative(seed=10, iterations=1, render=render) -272 -273if __name__=='__main__': -274 main() +211 env = gym.make(custom_env_id_ProMP, render_mode='human' if render else None) +212 +213 rewards = 0 +214 obs = env.reset() +215 +216 if render: +217 env.render() +218 +219 # number of samples/full trajectories (multiple environment steps) +220 for i in range(iterations): +221 ac = env.action_space.sample() +222 obs, reward, terminated, truncated, info = env.step(ac) +223 rewards += reward +224 +225 if terminated or truncated: +226 print(rewards) +227 rewards = 0 +228 obs = env.reset() +229 +230 if render: +231 env.render() +232 +233 rewards = 0 +234 obs = env.reset() +235 +236 # number of samples/full trajectories (multiple environment steps) +237 for i in range(iterations): +238 ac = env.action_space.sample() +239 obs, reward, terminated, truncated, info = env.step(ac) +240 rewards += reward +241 +242 if terminated or truncated: +243 print(rewards) +244 rewards = 0 +245 obs = env.reset() +246 +247 try: # Some mujoco-based envs don't correlcty implement .close +248 env.close() +249 except: +250 pass +251 +252 +253def main(render=False): +254 # DMP +255 example_mp("fancy_DMP/HoleReacher-v0", seed=10, iterations=5, render=render) +256 +257 # ProMP +258 example_mp("fancy_ProMP/HoleReacher-v0", seed=10, iterations=5, render=render) +259 example_mp("fancy_ProMP/BoxPushingTemporalSparse-v0", seed=10, iterations=1, render=render) +260 example_mp("fancy_ProMP/TableTennis4D-v0", seed=10, iterations=20, render=render) +261 +262 # ProDMP with Replanning +263 example_mp("fancy_ProDMP/BoxPushingDenseReplan-v0", seed=10, iterations=4, render=render) +264 example_mp("fancy_ProDMP/TableTennis4DReplan-v0", seed=10, iterations=20, render=render) +265 example_mp("fancy_ProDMP/TableTennisWindReplan-v0", seed=10, iterations=20, render=render) +266 +267 # Altered basis functions +268 obs1 = example_custom_mp("fancy_ProMP/Reacher5d-v0", seed=10, iterations=1, render=render) +269 +270 # Custom MP +271 example_fully_custom_mp(seed=10, iterations=1, render=render) +272 example_fully_custom_mp_alternative(seed=10, iterations=1, render=render) +273 +274if __name__=='__main__': +275 main()
diff --git a/docs/build/html/examples/mp_params_tuning.html b/docs/build/html/examples/mp_params_tuning.html index 14659d6..df5c2c7 100644 --- a/docs/build/html/examples/mp_params_tuning.html +++ b/docs/build/html/examples/mp_params_tuning.html @@ -4,7 +4,7 @@ - MP Params Tuning Example — Fancy Gym 0.2 documentation + MP Params Tuning Example — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/examples/open_ai.html b/docs/build/html/examples/open_ai.html index 74ceddc..fa220a1 100644 --- a/docs/build/html/examples/open_ai.html +++ b/docs/build/html/examples/open_ai.html @@ -4,7 +4,7 @@ - OpenAI Envs Examples — Fancy Gym 0.2 documentation + OpenAI Envs Examples — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
@@ -122,27 +122,27 @@ 13 Returns: 14 15 """ -16 env = gym.make(env_name) +16 env = gym.make(env_name, render_mode='human' if render else None) 17 18 returns = 0 19 obs = env.reset(seed=seed) 20 # number of samples/full trajectories (multiple environment steps) 21 for i in range(10): 22 if render and i % 2 == 0: -23 env.render(mode="human") -24 else: -25 env.render() -26 ac = env.action_space.sample() -27 obs, reward, terminated, truncated, info = env.step(ac) -28 returns += reward -29 -30 if terminated or truncated: -31 print(returns) -32 obs = env.reset() -33 +23 env.render() +24 ac = env.action_space.sample() +25 obs, reward, terminated, truncated, info = env.step(ac) +26 returns += reward +27 +28 if terminated or truncated: +29 print(returns) +30 obs = env.reset() +31 +32def main(render=True): +33 example_mp("gym_ProMP/Reacher-v2", render=render) 34 35if __name__ == '__main__': -36 example_mp("gym_ProMP/Reacher-v2") +36 main()
diff --git a/docs/build/html/examples/pd_control_gain_tuning.html b/docs/build/html/examples/pd_control_gain_tuning.html index 6467abc..b83ab3f 100644 --- a/docs/build/html/examples/pd_control_gain_tuning.html +++ b/docs/build/html/examples/pd_control_gain_tuning.html @@ -4,7 +4,7 @@ - PD Control Gain Tuning Example — Fancy Gym 0.2 documentation + PD Control Gain Tuning Example — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/examples/replanning_envs.html b/docs/build/html/examples/replanning_envs.html index 9c842fd..afdaf29 100644 --- a/docs/build/html/examples/replanning_envs.html +++ b/docs/build/html/examples/replanning_envs.html @@ -4,7 +4,7 @@ - Replanning Example — Fancy Gym 0.2 documentation + Replanning Example — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
@@ -112,24 +112,24 @@ 3 4 5def example_run_replanning_env(env_name="fancy_ProDMP/BoxPushingDenseReplan-v0", seed=1, iterations=1, render=False): - 6 env = gym.make(env_name) + 6 env = gym.make(env_name, render_mode='human' if render else None) 7 env.reset(seed=seed) 8 for i in range(iterations): - 9 done = False -10 while done is False: -11 ac = env.action_space.sample() -12 obs, reward, terminated, truncated, info = env.step(ac) -13 if render: -14 env.render(mode="human") -15 if terminated or truncated: -16 env.reset() + 9 while True: +10 ac = env.action_space.sample() +11 obs, reward, terminated, truncated, info = env.step(ac) +12 if render: +13 env.render() +14 if terminated or truncated: +15 env.reset() +16 break 17 env.close() 18 del env 19 20 21def example_custom_replanning_envs(seed=0, iteration=100, render=True): 22 # id for a step-based environment -23 base_env_id = "BoxPushingDense-v0" +23 base_env_id = "fancy/BoxPushingDense-v0" 24 25 wrappers = [fancy_gym.envs.mujoco.box_pushing.mp_wrapper.MPWrapper] 26 @@ -147,31 +147,34 @@ 38 'replanning_schedule': lambda pos, vel, obs, action, t: t % 25 == 0, 39 'condition_on_desired': True} 40 -41 env = fancy_gym.make_bb(env_id=base_env_id, wrappers=wrappers, black_box_kwargs=black_box_kwargs, -42 traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs, -43 phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs, -44 seed=seed) -45 if render: -46 env.render(mode="human") -47 -48 obs = env.reset() -49 -50 for i in range(iteration): -51 ac = env.action_space.sample() -52 obs, reward, terminated, truncated, info = env.step(ac) -53 if terminated or truncated: -54 env.reset() -55 -56 env.close() -57 del env -58 +41 base_env = gym.make(base_env_id, render_mode='human' if render else None) +42 env = fancy_gym.make_bb(env=base_env, wrappers=wrappers, black_box_kwargs=black_box_kwargs, +43 traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs, +44 phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs, +45 seed=seed) +46 if render: +47 env.render() +48 +49 obs = env.reset() +50 +51 for i in range(iteration): +52 ac = env.action_space.sample() +53 obs, reward, terminated, truncated, info = env.step(ac) +54 if terminated or truncated: +55 env.reset() +56 +57 env.close() +58 del env 59 -60if __name__ == "__main__": +60def main(render=False): 61 # run a registered replanning environment -62 example_run_replanning_env(env_name="fancy_ProDMP/BoxPushingDenseReplan-v0", seed=1, iterations=1, render=False) +62 example_run_replanning_env(env_name="fancy_ProDMP/BoxPushingDenseReplan-v0", seed=1, iterations=1, render=render) 63 64 # run a custom replanning environment -65 example_custom_replanning_envs(seed=0, iteration=8, render=True) +65 example_custom_replanning_envs(seed=0, iteration=8, render=render) +66 +67if __name__ == "__main__": +68 main()
diff --git a/docs/build/html/generated/fancy_gym.envs.html b/docs/build/html/generated/fancy_gym.envs.html index 0ec17e6..5d096e5 100644 --- a/docs/build/html/generated/fancy_gym.envs.html +++ b/docs/build/html/generated/fancy_gym.envs.html @@ -4,7 +4,7 @@ - fancy_gym.envs — Fancy Gym 0.2 documentation + fancy_gym.envs — Fancy Gym 0.3.0 documentation @@ -39,7 +39,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/generated/fancy_gym.register.html b/docs/build/html/generated/fancy_gym.register.html index 92be35d..643537a 100644 --- a/docs/build/html/generated/fancy_gym.register.html +++ b/docs/build/html/generated/fancy_gym.register.html @@ -4,7 +4,7 @@ - fancy_gym.register — Fancy Gym 0.2 documentation + fancy_gym.register — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/generated/fancy_gym.upgrade.html b/docs/build/html/generated/fancy_gym.upgrade.html index e72c7f5..6badcb5 100644 --- a/docs/build/html/generated/fancy_gym.upgrade.html +++ b/docs/build/html/generated/fancy_gym.upgrade.html @@ -4,7 +4,7 @@ - fancy_gym.upgrade — Fancy Gym 0.2 documentation + fancy_gym.upgrade — Fancy Gym 0.3.0 documentation @@ -40,7 +40,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index b9bad09..c64d420 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -3,7 +3,7 @@ - Index — Fancy Gym 0.2 documentation + Index — Fancy Gym 0.3.0 documentation @@ -38,7 +38,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/guide/basic_usage.html b/docs/build/html/guide/basic_usage.html index 7467226..8a13e38 100644 --- a/docs/build/html/guide/basic_usage.html +++ b/docs/build/html/guide/basic_usage.html @@ -4,7 +4,7 @@ - Basic Usage — Fancy Gym 0.2 documentation + Basic Usage — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/guide/episodic_rl.html b/docs/build/html/guide/episodic_rl.html index 3515aa7..78f99a3 100644 --- a/docs/build/html/guide/episodic_rl.html +++ b/docs/build/html/guide/episodic_rl.html @@ -4,7 +4,7 @@ - What is Episodic RL? — Fancy Gym 0.2 documentation + What is Episodic RL? — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
diff --git a/docs/build/html/guide/installation.html b/docs/build/html/guide/installation.html index b9baffe..b05be13 100644 --- a/docs/build/html/guide/installation.html +++ b/docs/build/html/guide/installation.html @@ -4,7 +4,7 @@ - Installation — Fancy Gym 0.2 documentation + Installation — Fancy Gym 0.3.0 documentation @@ -41,7 +41,7 @@
- 0.2 + 0.3.0
@@ -135,7 +135,7 @@ pip install '

Pip can not automatically install up-to-date versions of metaworld, since they are not avaible on PyPI yet. Install metaworld via

-
pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld
+
pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@c822f28f582ba1ad49eb5dcf61016566f28003ba#egg=metaworld
 

 

 
@@ -169,7 +169,7 @@ pip install -e

Metaworld has to be installed manually with

-
pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld
+
pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@c822f28f582ba1ad49eb5dcf61016566f28003ba#egg=metaworld
 

 

 
diff --git a/docs/build/html/guide/upgrading_envs.html b/docs/build/html/guide/upgrading_envs.html
index 3444d4e..8e0c2b9 100644
--- a/docs/build/html/guide/upgrading_envs.html
+++ b/docs/build/html/guide/upgrading_envs.html
@@ -4,7 +4,7 @@
   
 
   
-  Creating new MP Environments — Fancy Gym 0.2 documentation
+  Creating new MP Environments — Fancy Gym 0.3.0 documentation
       
       
       
@@ -41,7 +41,7 @@
               
           
               

-                0.2
+                0.3.0
               

 

   
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index 2430040..df9a562 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -4,7 +4,7 @@
   
 
   
-  Fancy Gym — Fancy Gym 0.2 documentation
+  Fancy Gym — Fancy Gym 0.3.0 documentation
       
       
       
@@ -40,7 +40,7 @@
               
           
               

-                0.2
+                0.3.0
               

 

   
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
index 372130a..7c889a6 100644
Binary files a/docs/build/html/objects.inv and b/docs/build/html/objects.inv differ
diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html
index 5df2185..e62f50e 100644
--- a/docs/build/html/py-modindex.html
+++ b/docs/build/html/py-modindex.html
@@ -3,7 +3,7 @@
 
   
   
-  Python Module Index — Fancy Gym 0.2 documentation
+  Python Module Index — Fancy Gym 0.3.0 documentation
       
       
       
@@ -41,7 +41,7 @@
               
           
               

-                0.2
+                0.3.0
               

 

   
diff --git a/docs/build/html/search.html b/docs/build/html/search.html
index b9ff6cd..c632ba3 100644
--- a/docs/build/html/search.html
+++ b/docs/build/html/search.html
@@ -3,7 +3,7 @@
 
   
   
-  Search — Fancy Gym 0.2 documentation
+  Search — Fancy Gym 0.3.0 documentation
       
       
       
@@ -41,7 +41,7 @@
               
           
               

-                0.2
+                0.3.0
               

 

   
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 2666042..209c151 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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17, "black_box": [17, 18], "raw_interface_wrapp": [17, 18], "registri": [17, 18], "defaultmpwrapp": [17, 18], "register_step_bas": 17, "bool": [17, 22], "dict": [17, 18], "kwarg": 17, "If": [17, 19, 21, 22, 23], "want": [17, 21, 23], "uniqu": [17, 18, 20], "identifi": [17, 18], "entri": 17, "srtep": 17, "dictionari": [17, 18, 19], "overrid": [17, 18], "keyword": 17, "constructor": 17, "note": [17, 18], "otherwis": [17, 18], "given": [17, 19, 22], "string": 17, "notat": 17, "warn": 17, "messag": 17, "suggest": 17, "exampl": [17, 18, 19, 22], "To": [17, 18, 19, 23], "myenv": [17, 18], "myenvclass": 17, "my_modul": 17, "expect": 18, "known_mp": 18, "Will": [18, 23], "match": [18, 22], "wish": 18, "one": [18, 22, 23], "alongsid": 18, "custommpwrapp": 18, "param": [18, 23], "prepar": 19, "ad": 19, "namespac": 19, "legaci": [19, 21], "rais": [19, 22], "metaworld": [19, 20, 21, 23], "n": 19, "cumul": 19, "part": [19, 22], "mainli": 19, "meant": 19, "debug": 19, "log": 19, "train": 19, "step_act": 19, "output": 19, "step_observ": 19, "intermedi": 19, "step_reward": 19, "trajectory_length": 19, "underli": 19, "origin": 19, "In": [19, 22], "miss": 19, "fill": 19, "_": 19, "keep": 19, "mind": 19, "process": 19, "split": 19, "lean": 19, "still": [19, 22], "beta": 19, "feel": [19, 22], "problem": 19, "occur": 19, "directli": [19, 22], "gym_": 19, "again": 19, "conveni": 19, "variabl": 19, "store": 19, "all_movement_primitive_environ": 19, "all_fancy_movement_primitive_environ": 19, "all_gym_movement_primitive_environ": 19, "deepmind": [19, 23], "all_dmc_movement_primitive_environ": 19, "all_metaworld_movement_primitive_environ": 19, "movement_primitive_environments_for_n": 19, "my_custom_namespac": 19, "tradit": 20, "concept": 20, "stochast": 20, "search": 20, "commonli": 20, "produc": 20, "like": [20, 21], "probabilist": [20, 23], "convert": 20, "track": 20, "pd": [20, 23], "tailor": 20, "addition": 20, "special": 20, "overarch": 20, "remain": 20, "polici": 20, "craft": 20, "accommod": 20, "contextu": [20, 22], "At": 20, "onset": 20, "subset": 20, "demand": 20, "virtual": 21, "venv": 21, "3rd": 21, "altern": [21, 23], "poetri": 21, "conda": 21, "few": 21, "choos": 21, "box2d": 21, "jax": 21, "automat": 21, "date": 21, "sinc": 21, "git": 21, "c822f28f582ba1ad49eb5dcf61016566f28003ba": 21, "egg": 21, "clone": 21, "repositori": 21, "go": 21, "folder": 21, "cd": 21, "manual": 21, "guid": 22, "explain": 22, "how": 22, "abc": 22, "abstractmethod": 22, "properti": 22, "context_mask": 22, "mask": 22, "filter": 22, "unwant": 22, "unnecessari": 22, "after": 22, "first": 22, "receiv": 22, "arrai": 22, "indic": 22, "ones": 22, "dtype": 22, "float": 22, "exclus": 22, "regardless": 22, "indirectli": 22, "notimplementederror": 22, "overitten": 22, "attribut": 22, "document": 22, "mp_pytorch": 22, "userguid": 22, "anoth": 22, "merg": 22, "num_basis_zero_go": 22, "rough": 22, "outlin": 22, "shown": 22, "simpli": 22, "cool_new_env": 22, "my_custom_mpwrapp": 22, "my_custom_env": 22, "custom_prodmp": 22, "built": 23, "fork": 23, "renown": 23, "librari": 23, "sever": 23, "etc": 23, "With": 23, "straightforward": 23, "transform": 23, "compat": 23, "contribut": 23, "own": 23, "re": 23, "inspir": 23, "assist": 23, "highli": 23, "randomli": 23, "sleep": 23, "metadata": 23, "render_fp": 23, "about": 23, "pypi": 23, "master": 23, "what": 23, "usag": 23, "tune": 23, "public": 23, "softwar": 23, "author": 23, "otto": 23, "fabian": 23, "celik": 23, "onur": 23, "roth": 23, "dominik": 23, "zhou": 23, "hongyi": 23, "abstract": 23, "unifi": 23, "approach": 23, "url": 23, "organ": 23, "autonom": 23, "lab": 23, "alr": 23, "kit": 23}, "objects": {"fancy_gym": [[16, 0, 0, "-", "envs"], [17, 1, 1, "", "register"], [18, 1, 1, "", "upgrade"]]}, "objtypes": {"0": "py:module", "1": "py:function"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"]}, "titleterms": {"api": [0, 23], "deepmind": [1, 8], "control": [1, 3, 8, 14], "dmc": 1, "step": [1, 3, 5, 6, 7, 19], "base": [1, 3, 5, 6, 7, 19], "environ": [1, 3, 5, 6, 7, 19, 22, 23], "mp": [1, 3, 5, 6, 7, 12, 22], "airhockei": 2, "classic": 3, "fanci": [4, 23], "mujoco": 5, "box": [5, 19], "push": 5, "tabl": 5, "tenni": 5, "beer": 5, "pong": 5, "variat": 5, "exist": 5, "metaworld": [6, 10], "gymnasium": 7, "exampl": [8, 9, 10, 11, 12, 13, 14, 15, 23], "gener": 9, "usag": [9, 19], "movement": 11, "primit": 11, "param": 12, "tune": [12, 14], "openai": 13, "env": [13, 16], "pd": 14, "gain": 14, "replan": 15, "fancy_gym": [16, 17, 18], "regist": 17, "upgrad": 18, "basic": 19, "black": 19, "what": 20, "i": 20, "episod": 20, "rl": 20, "instal": 21, "from": 21, "pypi": 21, "recommend": 21, "master": 21, "creat": 22, "new": 22, "gym": 23, "kei": 23, "featur": 23, "quickstart": 23, "guid": 23, "user": 23, "cite": 23, "project": 23, "icon": 23, "attribut": 23}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.viewcode": 1, "sphinx": 57}, "alltitles": {"API": [[0, "api"], [23, null]], "DeepMind Control (DMC)": [[1, "deepmind-control-dmc"]], "Step-Based Environments": [[1, "step-based-environments"], [3, "step-based-environments"], [5, "step-based-environments"], [6, "step-based-environments"], [7, "step-based-environments"], [19, "step-based-environments"]], "MP Environments": [[1, "mp-environments"], [3, "mp-environments"], [5, "mp-environments"], [6, "mp-environments"], [7, "mp-environments"]], "AirHockey": [[2, "airhockey"]], "Classic Control": [[3, "classic-control"]], "Fancy": [[4, "fancy"]], "Mujoco": [[5, "mujoco"]], "Box Pushing": [[5, "box-pushing"]], "Table Tennis": [[5, "table-tennis"]], "Beer Pong": [[5, "beer-pong"]], "Variations of existing environments": [[5, "variations-of-existing-environments"]], "Metaworld": [[6, "metaworld"]], "Gymnasium": [[7, "gymnasium"]], "DeepMind Control Examples": [[8, "deepmind-control-examples"]], "General Usage Examples": [[9, "general-usage-examples"]], "Metaworld Examples": [[10, "metaworld-examples"]], "Movement Primitives Examples": [[11, "movement-primitives-examples"]], "MP Params Tuning Example": [[12, "mp-params-tuning-example"]], "OpenAI Envs Examples": [[13, "openai-envs-examples"]], "PD Control Gain Tuning Example": [[14, "pd-control-gain-tuning-example"]], "Replanning Example": [[15, "replanning-example"]], "fancy_gym.envs": [[16, "module-fancy_gym.envs"]], "fancy_gym.register": [[17, "fancy-gym-register"]], "fancy_gym.upgrade": [[18, "fancy-gym-upgrade"]], "Basic Usage": [[19, "basic-usage"]], "Black-Box Environments": [[19, "black-box-environments"]], "What is Episodic RL?": [[20, "what-is-episodic-rl"]], "Installation": [[21, "installation"]], "Installation from PyPI (recommended)": [[21, "installation-from-pypi-recommended"]], "Installation from master": [[21, "installation-from-master"]], "Creating new MP Environments": [[22, "creating-new-mp-environments"]], "Fancy Gym": [[23, "fancy-gym"]], "Key Features": [[23, "key-features"]], "Quickstart Guide": [[23, "quickstart-guide"]], "User Guide": [[23, null]], "Environments": [[23, null]], "Examples": [[23, null]], "Citing the Project": [[23, "citing-the-project"]], "Icon Attribution": [[23, "icon-attribution"]]}, "indexentries": {"fancy_gym.envs": [[16, "module-fancy_gym.envs"]], "module": [[16, "module-fancy_gym.envs"]], "register() (in module fancy_gym)": [[17, "fancy_gym.register"]], "upgrade() (in module fancy_gym)": [[18, "fancy_gym.upgrade"]]}})
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 8ce3d31..0f17ef9 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,13 +1,17 @@
 # This conf.py is in large parts inspired by the oen used by stable-baselines 3
 
+import toml
 import datetime
 
 project = 'Fancy Gym'
 author = 'Fabian Otto, Onur Celik, Dominik Roth, Hongyi Zhou'
 copyright = f'2020-{datetime.date.today().year}, {author}'
 
-release = '0.2'  # The full version, including alpha/beta/rc tags
-version = '0.2'  # The short X.Y version
+pyproject_content = toml.load("../../pyproject.toml")
+proj_version = pyproject_content["project"]["version"]
+
+release = proj_version  # The full version, including alpha/beta/rc tags
+version = proj_version  # The short X.Y version
 
 extensions = [
     'myst_parser',
@@ -50,4 +54,4 @@ html_context = {
 }
 
 def setup(app):
-    app.add_css_file("style.css")
\ No newline at end of file
+    app.add_css_file("style.css")
diff --git a/docs/source/guide/installation.rst b/docs/source/guide/installation.rst
index 0077ba4..1c10973 100644
--- a/docs/source/guide/installation.rst
+++ b/docs/source/guide/installation.rst
@@ -32,7 +32,7 @@ since they are not avaible on PyPI yet. Install metaworld via
 
 .. code:: bash
 
-   pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld
+   pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@c822f28f582ba1ad49eb5dcf61016566f28003ba#egg=metaworld
 
 Installation from master
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -70,4 +70,4 @@ Metaworld has to be installed manually with
 
 .. code:: bash
 
-   pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld
\ No newline at end of file
+   pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@c822f28f582ba1ad49eb5dcf61016566f28003ba#egg=metaworld
diff --git a/fancy_gym/envs/mujoco/ant_jump/ant_jump.py b/fancy_gym/envs/mujoco/ant_jump/ant_jump.py
index 97cde0e..f89e0dd 100644
--- a/fancy_gym/envs/mujoco/ant_jump/ant_jump.py
+++ b/fancy_gym/envs/mujoco/ant_jump/ant_jump.py
@@ -115,6 +115,7 @@ 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
@@ -153,8 +154,15 @@ 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)
 
diff --git a/fancy_gym/envs/mujoco/beerpong/beerpong.py b/fancy_gym/envs/mujoco/beerpong/beerpong.py
index 802776f..e3af3fc 100644
--- a/fancy_gym/envs/mujoco/beerpong/beerpong.py
+++ b/fancy_gym/envs/mujoco/beerpong/beerpong.py
@@ -44,6 +44,7 @@ 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")
@@ -89,7 +90,7 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
             observation_space=self.observation_space,
             **kwargs
         )
-        utils.EzPickle.__init__(self)
+        self.render_active = False
 
     @property
     def start_pos(self):
@@ -169,8 +170,15 @@ 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()
diff --git a/fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py b/fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py
index a508d84..5d5c653 100644
--- a/fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py
+++ b/fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py
@@ -4,6 +4,7 @@ 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
 from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import calculate_jerk_profile, calculate_mean_squared_jerk, calculate_dimensionless_jerk, calculate_maximum_jerk
@@ -62,6 +63,7 @@ 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)
@@ -116,8 +118,15 @@ 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 calculate_smoothness_metrics(self, velocity_profile, dt):
         """
         Calculates the smoothness metrics for the given velocity profile.
diff --git a/fancy_gym/envs/mujoco/half_cheetah_jump/half_cheetah_jump.py b/fancy_gym/envs/mujoco/half_cheetah_jump/half_cheetah_jump.py
index 088f959..24d855d 100644
--- a/fancy_gym/envs/mujoco/half_cheetah_jump/half_cheetah_jump.py
+++ b/fancy_gym/envs/mujoco/half_cheetah_jump/half_cheetah_jump.py
@@ -60,7 +60,11 @@ 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):
     """
@@ -120,6 +124,9 @@ 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):
diff --git a/fancy_gym/envs/mujoco/hopper_jump/hopper_jump.py b/fancy_gym/envs/mujoco/hopper_jump/hopper_jump.py
index 96dd3a3..d6e7be3 100644
--- a/fancy_gym/envs/mujoco/hopper_jump/hopper_jump.py
+++ b/fancy_gym/envs/mujoco/hopper_jump/hopper_jump.py
@@ -88,6 +88,12 @@ class HopperEnvCustomXML(HopperEnv):
             **kwargs,
         )
 
+        self.render_active = False
+
+    def render(self):
+        self.render_active = True
+        return super().render()
+
 
 class HopperJumpEnv(HopperEnvCustomXML):
     """
@@ -201,6 +207,10 @@ 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):
diff --git a/fancy_gym/envs/mujoco/hopper_jump/hopper_jump_on_box.py b/fancy_gym/envs/mujoco/hopper_jump/hopper_jump_on_box.py
index c0c57c2..b56840b 100644
--- a/fancy_gym/envs/mujoco/hopper_jump/hopper_jump_on_box.py
+++ b/fancy_gym/envs/mujoco/hopper_jump/hopper_jump_on_box.py
@@ -140,6 +140,9 @@ 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):
diff --git a/fancy_gym/envs/mujoco/hopper_throw/hopper_throw.py b/fancy_gym/envs/mujoco/hopper_throw/hopper_throw.py
index 7a39cd8..bf9169e 100644
--- a/fancy_gym/envs/mujoco/hopper_throw/hopper_throw.py
+++ b/fancy_gym/envs/mujoco/hopper_throw/hopper_throw.py
@@ -61,6 +61,8 @@ 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)
@@ -94,8 +96,15 @@ 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)
 
diff --git a/fancy_gym/envs/mujoco/hopper_throw/hopper_throw_in_basket.py b/fancy_gym/envs/mujoco/hopper_throw/hopper_throw_in_basket.py
index 24ad402..56bbbec 100644
--- a/fancy_gym/envs/mujoco/hopper_throw/hopper_throw_in_basket.py
+++ b/fancy_gym/envs/mujoco/hopper_throw/hopper_throw_in_basket.py
@@ -68,6 +68,7 @@ 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):
 
@@ -118,8 +119,15 @@ 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)
 
diff --git a/fancy_gym/envs/mujoco/reacher/reacher.py b/fancy_gym/envs/mujoco/reacher/reacher.py
index f5af7f6..f3901a6 100644
--- a/fancy_gym/envs/mujoco/reacher/reacher.py
+++ b/fancy_gym/envs/mujoco/reacher/reacher.py
@@ -47,6 +47,8 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
                            **kwargs
                            )
 
+        self.render_active = False
+
     def step(self, action):
         self._steps += 1
 
@@ -77,8 +79,15 @@ 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)
diff --git a/fancy_gym/envs/mujoco/table_tennis/table_tennis_env.py b/fancy_gym/envs/mujoco/table_tennis/table_tennis_env.py
index e6a14f8..a586305 100644
--- a/fancy_gym/envs/mujoco/table_tennis/table_tennis_env.py
+++ b/fancy_gym/envs/mujoco/table_tennis/table_tennis_env.py
@@ -83,6 +83,8 @@ 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:
@@ -170,8 +172,15 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
 
         terminated, truncated = self._terminated, self._steps == MAX_EPISODE_STEPS_TABLE_TENNIS
 
+        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]
diff --git a/fancy_gym/envs/mujoco/walker_2d_jump/walker_2d_jump.py b/fancy_gym/envs/mujoco/walker_2d_jump/walker_2d_jump.py
index d9085ee..54ab00e 100644
--- a/fancy_gym/envs/mujoco/walker_2d_jump/walker_2d_jump.py
+++ b/fancy_gym/envs/mujoco/walker_2d_jump/walker_2d_jump.py
@@ -79,6 +79,8 @@ class Walker2dEnvCustomXML(Walker2dEnv):
             **kwargs,
         )
 
+        self.render_active = False
+
 
 class Walker2dJumpEnv(Walker2dEnvCustomXML):
     """
@@ -145,8 +147,15 @@ 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)
 
diff --git a/fancy_gym/examples/example_replanning_envs.py b/fancy_gym/examples/example_replanning_envs.py
index b06c970..47283a4 100644
--- a/fancy_gym/examples/example_replanning_envs.py
+++ b/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)
+    env = gym.make(env_name, render_mode='human' if render else None)
     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(mode="human")
+                env.render()
             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)
+    base_env = gym.make(base_env_id, render_mode='human' if render else None)
     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(mode="human")
+        env.render()
 
     obs = env.reset()
 
diff --git a/fancy_gym/examples/examples_dmc.py b/fancy_gym/examples/examples_dmc.py
index 2703e01..f02b7d9 100644
--- a/fancy_gym/examples/examples_dmc.py
+++ b/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)
+    env = gym.make(env_id, render_mode='human' if render else None)
     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(mode="human")
+            env.render()
         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)
+    base_env = gym.make(base_env_id, render_mode='human' if render else None)
     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(mode="human")
+        env.render()
 
     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 = True):
+def main(render = False):
     # # Standard DMC Suite tasks
     example_dmc("dm_control/fish-swim", seed=10, iterations=1000, render=render)
     #
diff --git a/fancy_gym/examples/examples_general.py b/fancy_gym/examples/examples_general.py
index 9def5b6..aff6b5c 100644
--- a/fancy_gym/examples/examples_general.py
+++ b/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)
+    env = gym.make(env_id, render_mode='human' if render else None)
     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 = True):
+def main(render = False):
     # Basic gym task
     example_general("Pendulum-v1", seed=10, iterations=200, render=render)
 
diff --git a/fancy_gym/examples/examples_metaworld.py b/fancy_gym/examples/examples_metaworld.py
index bd87c2b..f8b59cd 100644
--- a/fancy_gym/examples/examples_metaworld.py
+++ b/fancy_gym/examples/examples_metaworld.py
@@ -2,7 +2,7 @@ import gymnasium as gym
 import fancy_gym
 
 
-def example_meta(env_id="fish-swim", seed=1, iterations=1000, render=True):
+def example_meta(env_id="metaworld/button-press-v2", 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="fish-swim", seed=1, iterations=1000, render=True):
     Returns:
 
     """
-    env = gym.make(env_id)
+    env = gym.make(env_id, render_mode='human' if render else None)
     rewards = 0
     obs = env.reset(seed=seed)
     print("observation shape:", env.observation_space.shape)
@@ -27,9 +27,7 @@ def example_meta(env_id="fish-swim", seed=1, iterations=1000, render=True):
     for i in range(iterations):
         ac = env.action_space.sample()
         if 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)
+            env.render()
         obs, reward, terminated, truncated, info = env.step(ac)
         rewards += reward
         if terminated or truncated:
@@ -81,7 +79,7 @@ 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)
+    base_env = gym.make(base_env_id, render_mode='human' if render else None)
     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,
@@ -93,7 +91,7 @@ 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(mode="human")
+        env.render()
 
     rewards = 0
     obs = env.reset(seed=seed)
diff --git a/fancy_gym/examples/examples_open_ai.py b/fancy_gym/examples/examples_open_ai.py
index f1688ef..5dbd10e 100644
--- a/fancy_gym/examples/examples_open_ai.py
+++ b/fancy_gym/examples/examples_open_ai.py
@@ -13,15 +13,13 @@ def example_mp(env_name, seed=1, render=True):
     Returns:
 
     """
-    env = gym.make(env_name)
+    env = gym.make(env_name, render_mode='human' if render else None)
 
     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)
diff --git a/pyproject.toml b/pyproject.toml
index 20e40b0..f18b987 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "fancy_gym"
-version = "0.1.4"
+version = "0.3.0"
 description = "Fancy Gym: Unifying interface for various RL benchmarks with support for Black Box approaches."
 readme = "README.md"
 authors = [
@@ -26,6 +26,7 @@ classifiers = [
 ]
 
 dependencies = [
+    "toml",
     "mp_pytorch<=0.1.3",
     "mujoco==2.3.3",
     "gymnasium[mujoco]>=0.26.0"
diff --git a/setup.py b/setup.py
index 2bd077d..2a533fa 100644
--- a/setup.py
+++ b/setup.py
@@ -1,5 +1,6 @@
 # We still provide a setup.py for backwards compatability.
 # But the pyproject.toml should be prefered.
+import toml
 import itertools
 from pathlib import Path
 from typing import List
@@ -8,6 +9,9 @@ from setuptools import setup, find_packages
 
 print('[!] You are currently installing/building fancy_gym via setup.py. This is only provided for backwards-compatability. Please use the pyproject.toml instead.')
 
+pyproject_content = toml.load("pyproject.toml")
+project_version = pyproject_content["project"]["version"]
+
 # Environment-specific dependencies for dmc and metaworld
 extras = {
     'dmc': ['shimmy[dm-control]', 'Shimmy==1.0.0'],
@@ -38,7 +42,7 @@ def find_package_data(extensions_to_include: List[str]) -> List[str]:
 setup(
     author='Fabian Otto, Onur Celik, Dominik Roth, Hongyi Zhou',
     name='fancy_gym',
-    version='0.1.0',
+    version=project_version,
     classifiers=[
         'Development Status :: 4 - Beta',
         'Intended Audience :: Science/Research',
@@ -55,6 +59,7 @@ setup(
     ],
     extras_require=extras,
     install_requires=[
+        'toml',
         'mp_pytorch<=0.1.3',
         'mujoco==2.3.3',
         'gymnasium[mujoco]>=0.26.0'