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Fancy Gym
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< li class = "toctree-l1" > < a class = "reference internal" href = "guide/episodic_rl.html" > What is Episodic RL?< / a > < / li >
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< li class = "toctree-l1" > < a class = "reference internal" href = "envs/fancy/index.html" > Fancy< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "envs/dmc.html" > DeepMind Control (DMC)< / a > < / li >
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< h1 > Fancy Gym< a class = "headerlink" href = "#fancy-gym" title = "Permalink to this heading" > < / a > < / h1 >
< div style = "text-align: center;" >
< img src = "_static/imgs/fancy_namelogo.svg" style = "margin: 5%; width: 80%;" > < / a >
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/* Little Hack: We don't want to show the title (ugly), but need to define it since it also sets the pages metadata (for titlebar and stuff) */
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< / style > < p > Built upon the foundation of
< a class = "reference external" href = "https://gymnasium.farama.org/" > Gymnasium< / a > (a maintained fork of
OpenAI’ < code class = "docutils literal notranslate" > < span class = "pre" > fancy_gym< / span > < / code > offers a comprehensive
collection of reinforcement learning environments.< / p >
< section id = "key-features" >
< h2 > Key Features< a class = "headerlink" href = "#key-features" title = "Permalink to this heading" > < / a > < / h2 >
< blockquote >
< div > < ul class = "simple" >
< li > < p > < strong > New Challenging Environments< / strong > : < code class = "docutils literal notranslate" > < span class = "pre" > fancy_gym< / span > < / code > includes several new
environments (< a class = "reference external" href = "envs/fancy/mujoco.html#box-pushing" > Panda Box Pushing< / a > ,
< a class = "reference external" href = "envs/fancy/mujoco.html#table-tennis" > Table Tennis< / a > ,
< a class = "reference external" href = "envs/fancy/index.html" > etc.< / a > ) that present a higher degree of
difficulty, pushing the boundaries of reinforcement learning research.< / p > < / li >
< li > < p > < strong > Support for Movement Primitives< / strong > : < code class = "docutils literal notranslate" > < span class = "pre" > fancy_gym< / span > < / code > supports a range
of movement primitives (MPs), including Dynamic Movement Primitives
(DMPs), Probabilistic Movement Primitives (ProMP), and Probabilistic
Dynamic Movement Primitives (ProDMP).< / p > < / li >
< li > < p > < strong > Upgrade to Movement Primitives< / strong > : With our framework, it’
straightforward to transform standard Gymnasium environments into
environments that support movement primitives.< / p > < / li >
< li > < p > < strong > Benchmark Suite Compatibility< / strong > : < code class = "docutils literal notranslate" > < span class = "pre" > fancy_gym< / span > < / code > makes it easy to
access renowned benchmark suites such as < a class = "reference external" href = "envs/dmc.html" > DeepMind
Control< / a >
and < a class = "reference external" href = "envs/meta.html" > Metaworld< / a > , whether you want
to use them in the regular step-based setting or using MPs.< / p > < / li >
< li > < p > < strong > Contribute Your Own Environments< / strong > : If you’
custom gym environments, both step-based and with movement
primitives, this
< a class = "reference external" href = "guide/upgrading_envs.html" > guide< / a >
will assist you. We encourage and highly appreciate submissions via
PRs to integrate these environments into < code class = "docutils literal notranslate" > < span class = "pre" > fancy_gym< / span > < / code > .< / p > < / li >
< / ul >
< / div > < / blockquote >
< / section >
< section id = "quickstart-guide" >
< h2 > Quickstart Guide< a class = "headerlink" href = "#quickstart-guide" title = "Permalink to this heading" > < / a > < / h2 >
< p > Install via pip (< a class = "reference external" href = "guide/installation.html" > or use an alternative installation method< / a > )< / p >
< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > pip< span class = "w" > < / span > install< span class = "w" > < / span > < span class = "s1" > ' fancy_gym[all]' < / span >
< / pre > < / div >
< / div >
< p > Try out one of our step-based environments (< a class = "reference external" href = "envs/fancy/index.html" > or explore our other envs< / a > )< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > gymnasium< / span > < span class = "k" > as< / span > < span class = "nn" > gym< / span >
< span class = "kn" > import< / span > < span class = "nn" > fancy_gym< / span >
< span class = "kn" > import< / span > < span class = "nn" > time< / span >
< span class = "n" > env< / span > < span class = "o" > =< / span > < span class = "n" > gym< / span > < span class = "o" > .< / span > < span class = "n" > make< / span > < span class = "p" > (< / span > < span class = "s1" > ' fancy/BoxPushingDense-v0' < / span > < span class = "p" > ,< / span > < span class = "n" > render_mode< / span > < span class = "o" > =< / span > < span class = "s1" > ' human' < / span > < span class = "p" > )< / span >
< span class = "n" > observation< / span > < span class = "o" > =< / span > < span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > reset< / span > < span class = "p" > ()< / span >
< span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > render< / span > < span class = "p" > ()< / span >
< span class = "k" > for< / span > < span class = "n" > i< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 1000< / span > < span class = "p" > ):< / span >
< span class = "n" > action< / span > < span class = "o" > =< / span > < span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > action_space< / span > < span class = "o" > .< / span > < span class = "n" > sample< / span > < span class = "p" > ()< / span > < span class = "c1" > # Randomly sample an action< / span >
< span class = "n" > observation< / span > < span class = "p" > ,< / span > < span class = "n" > reward< / span > < span class = "p" > ,< / span > < span class = "n" > terminated< / span > < span class = "p" > ,< / span > < span class = "n" > truncated< / span > < span class = "p" > ,< / span > < span class = "n" > info< / span > < span class = "o" > =< / span > < span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > step< / span > < span class = "p" > (< / span > < span class = "n" > action< / span > < span class = "p" > )< / span >
< span class = "n" > time< / span > < span class = "o" > .< / span > < span class = "n" > sleep< / span > < span class = "p" > (< / span > < span class = "mi" > 1< / span > < span class = "o" > /< / span > < span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > metadata< / span > < span class = "p" > [< / span > < span class = "s1" > ' render_fps' < / span > < span class = "p" > ])< / span >
< span class = "k" > if< / span > < span class = "n" > terminated< / span > < span class = "ow" > or< / span > < span class = "n" > truncated< / span > < span class = "p" > :< / span >
< span class = "n" > observation< / span > < span class = "p" > ,< / span > < span class = "n" > info< / span > < span class = "o" > =< / span > < span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > reset< / span > < span class = "p" > ()< / span >
< / pre > < / div >
< / div >
< p > Explore the MP-based variant (< a class = "reference external" href = "guide/episodic_rl.html" > or learn more about Movement Primitives (MPs)< / a > )< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > gymnasium< / span > < span class = "k" > as< / span > < span class = "nn" > gym< / span >
< span class = "kn" > import< / span > < span class = "nn" > fancy_gym< / span >
< span class = "n" > env< / span > < span class = "o" > =< / span > < span class = "n" > gym< / span > < span class = "o" > .< / span > < span class = "n" > make< / span > < span class = "p" > (< / span > < span class = "s1" > ' fancy_ProMP/BoxPushingDense-v0' < / span > < span class = "p" > ,< / span > < span class = "n" > render_mode< / span > < span class = "o" > =< / span > < span class = "s1" > ' human' < / span > < span class = "p" > )< / span >
< span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > reset< / span > < span class = "p" > ()< / span >
< span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > render< / span > < span class = "p" > ()< / span >
< span class = "k" > for< / span > < span class = "n" > i< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 10< / span > < span class = "p" > ):< / span >
< span class = "n" > action< / span > < span class = "o" > =< / span > < span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > action_space< / span > < span class = "o" > .< / span > < span class = "n" > sample< / span > < span class = "p" > ()< / span > < span class = "c1" > # Randomly sample MP parameters< / span >
< span class = "n" > observation< / span > < span class = "p" > ,< / span > < span class = "n" > reward< / span > < span class = "p" > ,< / span > < span class = "n" > terminated< / span > < span class = "p" > ,< / span > < span class = "n" > truncated< / span > < span class = "p" > ,< / span > < span class = "n" > info< / span > < span class = "o" > =< / span > < span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > step< / span > < span class = "p" > (< / span > < span class = "n" > action< / span > < span class = "p" > )< / span > < span class = "c1" > # Will execute full trajectory, based on MP< / span >
< span class = "n" > observation< / span > < span class = "o" > =< / span > < span class = "n" > env< / span > < span class = "o" > .< / span > < span class = "n" > reset< / span > < span class = "p" > ()< / span >
< / pre > < / div >
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< p class = "caption" role = "heading" > < span class = "caption-text" > User Guide< / span > < / p >
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< li class = "toctree-l1" > < a class = "reference internal" href = "guide/installation.html" > Installation< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "guide/installation.html#installation-from-pypi-recommended" > Installation from PyPI (recommended)< / a > < / li >
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< li class = "toctree-l1" > < a class = "reference internal" href = "guide/episodic_rl.html" > What is Episodic RL?< / a > < / li >
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< li class = "toctree-l2" > < a class = "reference internal" href = "guide/basic_usage.html#step-based-environments" > Step-Based Environments< / a > < / li >
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< li class = "toctree-l1" > < a class = "reference internal" href = "guide/upgrading_envs.html" > Creating new MP Environments< / a > < / li >
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< p class = "caption" role = "heading" > < span class = "caption-text" > Environments< / span > < / p >
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< li class = "toctree-l1" > < a class = "reference internal" href = "envs/fancy/index.html" > Fancy< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "envs/fancy/mujoco.html" > Mujoco< / a > < ul >
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< li class = "toctree-l3" > < a class = "reference internal" href = "envs/fancy/mujoco.html#step-based-environments" > Step-Based Environments< / a > < / li >
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< li class = "toctree-l3" > < a class = "reference internal" href = "envs/fancy/mujoco.html#mp-environments" > MP Environments< / a > < / li >
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< li class = "toctree-l2" > < a class = "reference internal" href = "envs/fancy/airhockey.html" > AirHockey< / a > < / li >
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< li class = "toctree-l3" > < a class = "reference internal" href = "envs/fancy/classic_control.html#step-based-environments" > Step-Based Environments< / a > < / li >
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< li class = "toctree-l3" > < a class = "reference internal" href = "envs/fancy/classic_control.html#mp-environments" > MP Environments< / a > < / li >
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< / ul >
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< li class = "toctree-l1" > < a class = "reference internal" href = "envs/dmc.html" > DeepMind Control (DMC)< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "envs/dmc.html#step-based-environments" > Step-Based Environments< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "envs/dmc.html#mp-environments" > MP Environments< / a > < / li >
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< / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "envs/meta.html" > Metaworld< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "envs/meta.html#step-based-environments" > Step-Based Environments< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "envs/meta.html#mp-environments" > MP Environments< / a > < / li >
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< li class = "toctree-l2" > < a class = "reference internal" href = "envs/open_ai.html#step-based-environments" > Step-Based Environments< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "envs/open_ai.html#mp-environments" > MP Environments< / a > < / li >
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< li class = "toctree-l1" > < a class = "reference internal" href = "examples/general.html" > General Usage Examples< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "examples/dmc.html" > DeepMind Control Examples< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "examples/metaworld.html" > Metaworld Examples< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "examples/open_ai.html" > OpenAI Envs Examples< / a > < / li >
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< li class = "toctree-l1" > < a class = "reference internal" href = "examples/mp_params_tuning.html" > MP Params Tuning Example< / a > < / li >
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< h2 > Citing the Project< a class = "headerlink" href = "#citing-the-project" title = "Permalink to this heading" > < / a > < / h2 >
< p > To cite this repository in publications:< / p >
< div class = "highlight-bibtex notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "nc" > @software< / span > < span class = "p" > {< / span > < span class = "nl" > fancy_gym< / span > < span class = "p" > ,< / span >
< span class = "w" > < / span > < span class = "na" > title< / span > < span class = "w" > < / span > < span class = "p" > =< / span > < span class = "w" > < / span > < span class = "s" > {Fancy Gym}< / span > < span class = "p" > ,< / span >
< span class = "w" > < / span > < span class = "na" > author< / span > < span class = "w" > < / span > < span class = "p" > =< / span > < span class = "w" > < / span > < span class = "s" > {Otto, Fabian and Celik, Onur and Roth, Dominik and Zhou, Hongyi}< / span > < span class = "p" > ,< / span >
< span class = "w" > < / span > < span class = "na" > abstract< / span > < span class = "w" > < / span > < span class = "p" > =< / span > < span class = "w" > < / span > < span class = "s" > {Fancy Gym: Unifying interface for various RL benchmarks with support for Black Box approaches.}< / span > < span class = "p" > ,< / span >
< span class = "w" > < / span > < span class = "na" > url< / span > < span class = "w" > < / span > < span class = "p" > =< / span > < span class = "w" > < / span > < span class = "s" > {https://github.com/ALRhub/fancy_gym}< / span > < span class = "p" > ,< / span >
< span class = "w" > < / span > < span class = "na" > organization< / span > < span class = "w" > < / span > < span class = "p" > =< / span > < span class = "w" > < / span > < span class = "s" > {Autonomous Learning Robots Lab (ALR) at KIT}< / span > < span class = "p" > ,< / span >
< span class = "p" > }< / span >
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< h2 > Icon Attribution< a class = "headerlink" href = "#icon-attribution" title = "Permalink to this heading" > < / a > < / h2 >
< p > The icon is based on the
< a class = "reference external" href = "https://github.com/Farama-Foundation/Gymnasium" > Gymnasium< / a > icon as
can be found
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