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328 lines
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Fancy Gym
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0.2
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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="guide/basic_usage.html">Basic Usage</a></li>
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<li class="toctree-l1"><a class="reference internal" href="envs/dmc.html">DeepMind Control (DMC)</a></li>
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<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="examples/general.html">General Usage Examples</a></li>
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<li class="toctree-l1"><a class="reference internal" href="examples/dmc.html">DeepMind Control Examples</a></li>
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<li class="toctree-l1"><a class="reference internal" href="examples/metaworld.html">Metaworld Examples</a></li>
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<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/movement_primitives.html">Movement Primitives 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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<li class="toctree-l1"><a class="reference internal" href="examples/pd_control_gain_tuning.html">PD Control Gain Tuning Example</a></li>
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<section id="fancy-gym">
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<h1>Fancy Gym<a class="headerlink" href="#fancy-gym" title="Permalink to this heading"></a></h1>
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<div style="text-align: center;">
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<img src="_static/imgs/fancy_namelogo.svg" style="margin: 5%; width: 80%;"></a>
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</div>
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<style>
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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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h1 {
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display: none;
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}
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</style><p>Built upon the foundation of
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<a class="reference external" href="https://gymnasium.farama.org/">Gymnasium</a> (a maintained fork of
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OpenAI’s renowned Gym library) <code class="docutils literal notranslate"><span class="pre">fancy_gym</span></code> offers a comprehensive
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collection of reinforcement learning environments.</p>
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<section id="key-features">
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<h2>Key Features<a class="headerlink" href="#key-features" title="Permalink to this heading"></a></h2>
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<blockquote>
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<div><ul class="simple">
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<li><p><strong>New Challenging Environments</strong>: <code class="docutils literal notranslate"><span class="pre">fancy_gym</span></code> includes several new
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environments (<a class="reference external" href="envs/fancy/mujoco.html#box-pushing">Panda Box Pushing</a>,
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<a class="reference external" href="envs/fancy/mujoco.html#table-tennis">Table Tennis</a>,
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<a class="reference external" href="envs/fancy/index.html">etc.</a>) that present a higher degree of
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difficulty, pushing the boundaries of reinforcement learning research.</p></li>
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<li><p><strong>Support for Movement Primitives</strong>: <code class="docutils literal notranslate"><span class="pre">fancy_gym</span></code> supports a range
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of movement primitives (MPs), including Dynamic Movement Primitives
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(DMPs), Probabilistic Movement Primitives (ProMP), and Probabilistic
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Dynamic Movement Primitives (ProDMP).</p></li>
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<li><p><strong>Upgrade to Movement Primitives</strong>: With our framework, it’s
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straightforward to transform standard Gymnasium environments into
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environments that support movement primitives.</p></li>
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<li><p><strong>Benchmark Suite Compatibility</strong>: <code class="docutils literal notranslate"><span class="pre">fancy_gym</span></code> makes it easy to
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access renowned benchmark suites such as <a class="reference external" href="envs/dmc.html">DeepMind
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Control</a>
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and <a class="reference external" href="envs/meta.html">Metaworld</a>, whether you want
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to use them in the regular step-based setting or using MPs.</p></li>
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<li><p><strong>Contribute Your Own Environments</strong>: If you’re inspired to create
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custom gym environments, both step-based and with movement
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primitives, this
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<a class="reference external" href="guide/upgrading_envs.html">guide</a>
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will assist you. We encourage and highly appreciate submissions via
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PRs to integrate these environments into <code class="docutils literal notranslate"><span class="pre">fancy_gym</span></code>.</p></li>
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</ul>
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</div></blockquote>
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</section>
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<section id="quickstart-guide">
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<h2>Quickstart Guide<a class="headerlink" href="#quickstart-guide" title="Permalink to this heading"></a></h2>
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<p>Install via pip (<a class="reference external" href="guide/installation.html">or use an alternative installation method</a>)</p>
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<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>
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</pre></div>
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</div>
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<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>
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<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>
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<span class="kn">import</span> <span class="nn">fancy_gym</span>
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<span class="kn">import</span> <span class="nn">time</span>
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<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>
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<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>
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<span class="n">env</span><span class="o">.</span><span class="n">render</span><span class="p">()</span>
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<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>
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<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>
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<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>
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<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>
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<span class="k">if</span> <span class="n">terminated</span> <span class="ow">or</span> <span class="n">truncated</span><span class="p">:</span>
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<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>
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</pre></div>
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</div>
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<p>Explore the MP-based variant (<a class="reference external" href="guide/episodic_rl.html">or learn more about Movement Primitives (MPs)</a>)</p>
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<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>
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<span class="kn">import</span> <span class="nn">fancy_gym</span>
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<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>
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<span class="n">env</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
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<span class="n">env</span><span class="o">.</span><span class="n">render</span><span class="p">()</span>
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<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>
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<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>
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<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>
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<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>
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</pre></div>
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</div>
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<div class="toctree-wrapper compound">
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<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="guide/installation.html">Installation</a><ul>
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<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-l2"><a class="reference internal" href="guide/installation.html#installation-from-master">Installation from master</a></li>
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</ul>
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</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-l1"><a class="reference internal" href="guide/basic_usage.html">Basic Usage</a><ul>
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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-l2"><a class="reference internal" href="guide/basic_usage.html#black-box-environments">Black-Box Environments</a></li>
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</ul>
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</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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</ul>
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</div>
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<div class="toctree-wrapper compound">
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<p class="caption" role="heading"><span class="caption-text">Environments</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="envs/fancy/index.html">Fancy</a><ul>
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<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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</ul>
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</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-l2"><a class="reference internal" href="envs/fancy/classic_control.html">Classic Control</a><ul>
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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>
|
||
</ul>
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</li>
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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>
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||
<li class="toctree-l2"><a class="reference internal" href="envs/dmc.html#mp-environments">MP Environments</a></li>
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||
</ul>
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</li>
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||
<li class="toctree-l1"><a class="reference internal" href="envs/meta.html">Metaworld</a><ul>
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||
<li class="toctree-l2"><a class="reference internal" href="envs/meta.html#step-based-environments">Step-Based Environments</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="envs/meta.html#mp-environments">MP Environments</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toctree-l1"><a class="reference internal" href="envs/open_ai.html">Gymnasium</a><ul>
|
||
<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>
|
||
</ul>
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||
</li>
|
||
</ul>
|
||
</div>
|
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<div class="toctree-wrapper compound">
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<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
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||
<ul>
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<li class="toctree-l1"><a class="reference internal" href="examples/general.html">General Usage Examples</a></li>
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||
<li class="toctree-l1"><a class="reference internal" href="examples/dmc.html">DeepMind Control Examples</a></li>
|
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<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>
|
||
<li class="toctree-l1"><a class="reference internal" href="examples/movement_primitives.html">Movement Primitives 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>
|
||
<li class="toctree-l1"><a class="reference internal" href="examples/pd_control_gain_tuning.html">PD Control Gain Tuning Example</a></li>
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<li class="toctree-l1"><a class="reference internal" href="examples/replanning_envs.html">Replanning Example</a></li>
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</ul>
|
||
</div>
|
||
<div class="toctree-wrapper compound">
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<p class="caption" role="heading"><span class="caption-text">API</span></p>
|
||
<ul>
|
||
<li class="toctree-l1"><a class="reference internal" href="api.html">API</a><ul>
|
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<li class="toctree-l2"><a class="reference internal" href="generated/fancy_gym.register.html">fancy_gym.register</a><ul>
|
||
<li class="toctree-l3"><a class="reference internal" href="generated/fancy_gym.register.html#fancy_gym.register"><code class="docutils literal notranslate"><span class="pre">register()</span></code></a></li>
|
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</ul>
|
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</li>
|
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<li class="toctree-l2"><a class="reference internal" href="generated/fancy_gym.upgrade.html">fancy_gym.upgrade</a><ul>
|
||
<li class="toctree-l3"><a class="reference internal" href="generated/fancy_gym.upgrade.html#fancy_gym.upgrade"><code class="docutils literal notranslate"><span class="pre">upgrade()</span></code></a></li>
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</ul>
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</li>
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</ul>
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</li>
|
||
</ul>
|
||
</div>
|
||
</section>
|
||
<section id="citing-the-project">
|
||
<h2>Citing the Project<a class="headerlink" href="#citing-the-project" title="Permalink to this heading"></a></h2>
|
||
<p>To cite <cite>fancy_gym</cite> 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>
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
<section id="icon-attribution">
|
||
<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
|
||
<a class="reference external" href="https://gymnasium.farama.org/_static/img/gymnasium_black.svg">here</a>.</p>
|
||
<hr class="docutils" />
|
||
<div style="text-align: center; background: #f8f8f8; border-radius: 10px;">
|
||
<a href="https://alr.iar.kit.edu/"><img src="_static/imgs/alr.svg" style="margin: 5%; width: 20%;"></a>
|
||
<a href="https://www.kit.edu/"><img src="_static/imgs/kit.svg" style="margin: 5%; width: 20%;"></a>
|
||
<a href="https://uni-tuebingen.de/"><img src="_static/imgs/uni_tuebingen.svg" style="margin: 5%; width: 20%;"></a>
|
||
</div>
|
||
<br></section>
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||
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||
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||
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||
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