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<section id="basic-usage">
<h1>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to this heading"></a></h1>
<p>We will only show the basics here and prepared <a class="reference internal" href="../examples/general.html#example-general"><span class="std std-ref">multiple examples</span></a> for a more detailed look.</p>
<section id="step-based-environments">
<h2>Step-Based Environments<a class="headerlink" href="#step-based-environments" title="Permalink to this heading"></a></h2>
<p>Regular step based environments added by Fancy Gym are added into the
<code class="docutils literal notranslate"><span class="pre">fancy/</span></code> namespace.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Legacy versions of Fancy Gym used <code class="docutils literal notranslate"><span class="pre">fancy_gym.make(...)</span></code>. This is no longer supported and will raise an Exception on new versions.</p>
</div>
<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">&#39;fancy/Reacher5d-v0&#39;</span><span class="p">)</span>
<span class="c1"># or env = gym.make(&#39;metaworld/reach-v2&#39;) # fancy_gym allows access to all metaworld ML1 tasks via the metaworld/ NS</span>
<span class="c1"># or env = gym.make(&#39;dm_control/ball_in_cup-catch-v0&#39;)</span>
<span class="c1"># or env = gym.make(&#39;Reacher-v2&#39;)</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">seed</span><span class="o">=</span><span class="mi">1</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="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="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">5</span> <span class="o">==</span> <span class="mi">0</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">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>
</section>
<section id="black-box-environments">
<h2>Black-Box Environments<a class="headerlink" href="#black-box-environments" title="Permalink to this heading"></a></h2>
<p>All environments provide by default the cumulative episode reward, this
can however be changed if necessary. Optionally, each environment
returns all collected information from each step as part of the infos.
This information is, however, mainly meant for debugging as well as
logging and not for training.</p>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Key</p></th>
<th class="head"><p>Description</p></th>
<th class="head"><p>Type</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><cite>positions</cite></p></td>
<td><p>Generated trajectory from MP</p></td>
<td><p>Optional</p></td>
</tr>
<tr class="row-odd"><td><p><cite>velocities</cite></p></td>
<td><p>Generated trajectory from MP</p></td>
<td><p>Optional</p></td>
</tr>
<tr class="row-even"><td><p><cite>step_actions</cite></p></td>
<td><p>Step-wise executed action based on controller output</p></td>
<td><p>Optional</p></td>
</tr>
<tr class="row-odd"><td><p><cite>step_observations</cite></p></td>
<td><p>Step-wise intermediate observations</p></td>
<td><p>Optional</p></td>
</tr>
<tr class="row-even"><td><p><cite>step_rewards</cite></p></td>
<td><p>Step-wise rewards</p></td>
<td><p>Optional</p></td>
</tr>
<tr class="row-odd"><td><p><cite>trajectory_length</cite></p></td>
<td><p>Total number of environment interactions</p></td>
<td><p>Always</p></td>
</tr>
<tr class="row-even"><td><p><cite>other</cite></p></td>
<td><p>All other information from the underlying environment are returned as a list with length <cite>trajectory_length</cite> maintaining the original key.
In case some information are not provided every time step, the missing values are filled with <cite>None</cite>.</p></td>
<td><p>Always</p></td>
</tr>
</tbody>
</table>
<p>Existing MP tasks can be created the same way as above. The namespace of
a MP-variant of an environment is given by
<code class="docutils literal notranslate"><span class="pre">&lt;original</span> <span class="pre">namespace&gt;_&lt;MP</span> <span class="pre">name&gt;/</span></code>. Just keep in mind, calling
<code class="docutils literal notranslate"><span class="pre">step()</span></code> executes a full trajectory.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Currently, we are also in the process of enabling replanning as
well as learning of sub-trajectories. This allows to split the
episode into multiple trajectories and is a hybrid setting between
step-based and black-box leaning. While this is already
implemented, it is still in beta and requires further testing. Feel
free to try it and open an issue with any problems that occur.</p>
</div>
<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">&#39;fancy_ProMP/Reacher5d-v0&#39;</span><span class="p">)</span>
<span class="c1"># or env = gym.make(&#39;metaworld_ProDMP/reach-v2&#39;)</span>
<span class="c1"># or env = gym.make(&#39;dm_control_DMP/ball_in_cup-catch-v0&#39;)</span>
<span class="c1"># or env = gym.make(&#39;gym_ProMP/Reacher-v2&#39;) # mp versions of envs added directly by gymnasium are in the gym_&lt;MP-type&gt; NS</span>
<span class="c1"># render() can be called once in the beginning with all necessary arguments.</span>
<span class="c1"># To turn it of again just call render() without any arguments.</span>
<span class="n">env</span><span class="o">.</span><span class="n">render</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;human&#39;</span><span class="p">)</span>
<span class="c1"># This returns the context information, not the full state observation</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><span class="n">seed</span><span class="o">=</span><span class="mi">1</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">5</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="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"># terminated or truncated is always True as we are working on the episode level, hence we always reset()</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>To show all available environments, we provide some additional
convenience variables. All of them return a dictionary with the keys
<code class="docutils literal notranslate"><span class="pre">DMP</span></code>, <code class="docutils literal notranslate"><span class="pre">ProMP</span></code>, <code class="docutils literal notranslate"><span class="pre">ProDMP</span></code> and <code class="docutils literal notranslate"><span class="pre">all</span></code> that store a list of
available environment ids.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">fancy_gym</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;All Black-box tasks:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fancy_gym</span><span class="o">.</span><span class="n">ALL_MOVEMENT_PRIMITIVE_ENVIRONMENTS</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fancy Black-box tasks:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fancy_gym</span><span class="o">.</span><span class="n">ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;OpenAI Gym Black-box tasks:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fancy_gym</span><span class="o">.</span><span class="n">ALL_GYM_MOVEMENT_PRIMITIVE_ENVIRONMENTS</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deepmind Control Black-box tasks:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fancy_gym</span><span class="o">.</span><span class="n">ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MetaWorld Black-box tasks:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fancy_gym</span><span class="o">.</span><span class="n">ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;If you add custom envs, their mp versions will be found in:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fancy_gym</span><span class="o">.</span><span class="n">MOVEMENT_PRIMITIVE_ENVIRONMENTS_FOR_NS</span><span class="p">[</span><span class="s1">&#39;&lt;my_custom_namespace&gt;&#39;</span><span class="p">])</span>
</pre></div>
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<h1>What is Episodic RL?<a class="headerlink" href="#what-is-episodic-rl" title="Permalink to this heading"></a></h1>
<div class="justify"><p>Movement primitive (MP) environments differ from traditional step-based
environments. They align more with concepts from stochastic search,
black-box optimization, and methods commonly found in classical robotics
and control. Instead of individual steps, MP environments operate on an
episode basis, executing complete trajectories. These trajectories are
produced by trajectory generators like Dynamic Movement Primitives
(DMP), Probabilistic Movement Primitives (ProMP) or Probabilistic
Dynamic Movement Primitives (ProDMP).</p>
<p>Once generated, these trajectories are converted into step-by-step
actions using a trajectory tracking controller. The specific controller
chosen depends on the environment’s requirements. Currently, we support
position, velocity, and PD-Controllers tailored for position, velocity,
and torque control. Additionally, we have a specialized controller
designed for the MetaWorld control suite.</p>
<p>While the overarching objective of MP environments remains the learning
of an optimal policy, the actions here represent the parametrization of
motion primitives to craft the right trajectory. Our framework further
enhances this by accommodating a contextual setting. At the episode’s
onset, we present the context space—a subset of the observation space.
This demands the prediction of a new action or MP parametrization for
every unique context.</p>
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<section id="installation">
<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We recommend installing <code class="docutils literal notranslate"><span class="pre">fancy_gym</span></code> into a virtual environment as
provided by <a class="reference external" href="https://docs.python.org/3/library/venv.html">venv</a>. 3rd
party alternatives to venv like <a class="reference external" href="https://python-poetry.org/">Poetry</a>
or <a class="reference external" href="https://docs.conda.io/en/latest/">Conda</a> can also be used.</p>
</div>
<section id="installation-from-pypi-recommended">
<h2>Installation from PyPI (recommended)<a class="headerlink" href="#installation-from-pypi-recommended" title="Permalink to this heading"></a></h2>
<p>Install <code class="docutils literal notranslate"><span class="pre">fancy_gym</span></code> via</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>fancy_gym
</pre></div>
</div>
<p>We have a few optional dependencies. If you also want to install those
use</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># to install all optional dependencies</span>
pip<span class="w"> </span>install<span class="w"> </span><span class="s1">&#39;fancy_gym[all]&#39;</span>
<span class="c1"># or choose only those you want</span>
pip<span class="w"> </span>install<span class="w"> </span><span class="s1">&#39;fancy_gym[dmc,box2d,mujoco-legacy,jax,testing]&#39;</span>
</pre></div>
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<p>Pip can not automatically install up-to-date versions of metaworld,
since they are not avaible on PyPI yet. Install metaworld via</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg<span class="o">=</span>metaworld
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<section id="installation-from-master">
<h2>Installation from master<a class="headerlink" href="#installation-from-master" title="Permalink to this heading"></a></h2>
<ol class="arabic simple">
<li><p>Clone the repository</p></li>
</ol>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>git@github.com:ALRhub/fancy_gym.git
</pre></div>
</div>
<ol class="arabic simple" start="2">
<li><p>Go to the folder</p></li>
</ol>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>fancy_gym
</pre></div>
</div>
<ol class="arabic simple" start="3">
<li><p>Install with</p></li>
</ol>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.
</pre></div>
</div>
<p>We have a few optional dependencies. If you also want to install those
use</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># to install all optional dependencies</span>
pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span><span class="s1">&#39;.[all]&#39;</span>
<span class="c1"># or choose only those you want</span>
pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span><span class="s1">&#39;.[dmc,box2d,mujoco-legacy,jax,testing]&#39;</span>
</pre></div>
</div>
<p>Metaworld has to be installed manually with</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg<span class="o">=</span>metaworld
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<section id="creating-new-mp-environments">
<h1>Creating new MP Environments<a class="headerlink" href="#creating-new-mp-environments" title="Permalink to this heading"></a></h1>
<p>This guide will explain to you how to upgrade an existing step-based Gymnasium environment into one, that supports Movement Primitives (MPs). If you are looking for a guide to build such a Gymnasium environment instead, please have a look at <a class="reference external" href="https://gymnasium.farama.org/tutorials/gymnasium_basics/environment_creation/">this guide</a>.</p>
<p>In case a required task is not supported yet in the MP framework, it can
be created relatively easy. For the task at hand, the following
<a class="reference external" href="https://github.com/ALRhub/fancy_gym/tree/master/fancy_gym/black_box/raw_interface_wrapper.py">interface</a>
needs to be implemented.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Tuple</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">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="k">class</span> <span class="nc">RawInterfaceWrapper</span><span class="p">(</span><span class="n">gym</span><span class="o">.</span><span class="n">Wrapper</span><span class="p">):</span>
<span class="n">mp_config</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">&#39;ProMP&#39;</span><span class="p">:</span> <span class="p">{},</span>
<span class="s1">&#39;DMP&#39;</span><span class="p">:</span> <span class="p">{},</span>
<span class="s1">&#39;ProDMP&#39;</span><span class="p">:</span> <span class="p">{},</span>
<span class="p">}</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">context_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns boolean mask of the same shape as the observation space.</span>
<span class="sd"> It determines whether the observation is returned for the contextual case or not.</span>
<span class="sd"> This effectively allows to filter unwanted or unnecessary observations from the full step-based case.</span>
<span class="sd"> E.g. Velocities starting at 0 are only changing after the first action. Given we only receive the</span>
<span class="sd"> context/part of the first observation, the velocities are not necessary in the observation for the task.</span>
<span class="sd"> Returns:</span>
<span class="sd"> bool array representing the indices of the observations</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="o">.</span><span class="n">observation_space</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">current_pos</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the current position of the action/control dimension.</span>
<span class="sd"> The dimensionality has to match the action/control dimension.</span>
<span class="sd"> This is not required when exclusively using velocity control,</span>
<span class="sd"> it should, however, be implemented regardless.</span>
<span class="sd"> E.g. The joint positions that are directly or indirectly controlled by the action.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
<span class="nd">@property</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">current_vel</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the current velocity of the action/control dimension.</span>
<span class="sd"> The dimensionality has to match the action/control dimension.</span>
<span class="sd"> This is not required when exclusively using position control,</span>
<span class="sd"> it should, however, be implemented regardless.</span>
<span class="sd"> E.g. The joint velocities that are directly or indirectly controlled by the action.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
</pre></div>
</div>
<p>Default configurations for MPs can be overitten by defining attributes
in mp_config. Available parameters are documented in the <a class="reference external" href="https://github.com/ALRhub/MP_PyTorch/blob/main/doc/README.md">MP_PyTorch
Userguide</a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">RawInterfaceWrapper</span><span class="p">(</span><span class="n">gym</span><span class="o">.</span><span class="n">Wrapper</span><span class="p">):</span>
<span class="n">mp_config</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">&#39;ProMP&#39;</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">&#39;phase_generator_kwargs&#39;</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">&#39;phase_generator_type&#39;</span><span class="p">:</span> <span class="s1">&#39;linear&#39;</span>
<span class="c1"># When selecting another generator type, the default configuration will not be merged for the attribute.</span>
<span class="p">},</span>
<span class="s1">&#39;controller_kwargs&#39;</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">&#39;p_gains&#39;</span><span class="p">:</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]),</span>
<span class="s1">&#39;d_gains&#39;</span><span class="p">:</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]),</span>
<span class="p">},</span>
<span class="s1">&#39;basis_generator_kwargs&#39;</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">&#39;num_basis&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
<span class="s1">&#39;num_basis_zero_start&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s1">&#39;num_basis_zero_goal&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="p">},</span>
<span class="p">},</span>
<span class="s1">&#39;DMP&#39;</span><span class="p">:</span> <span class="p">{},</span>
<span class="s1">&#39;ProDMP&#39;</span><span class="p">:</span> <span class="p">{}</span><span class="o">.</span>
<span class="p">}</span>
<span class="p">[</span><span class="o">...</span><span class="p">]</span>
</pre></div>
</div>
<p>If you created a new task wrapper, feel free to open a PR, so we can
integrate it for others to use as well. Without the integration the task
can still be used. A rough outline can be shown here, for more details
we recommend having a look at the
<a class="reference internal" href="../examples/movement_primitives.html#example-mp"><span class="std std-ref">multiple examples</span></a>.</p>
<p>If the step-based is already registered with gym, you can simply do the
following:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">fancy_gym</span><span class="o">.</span><span class="n">upgrade</span><span class="p">(</span>
<span class="nb">id</span><span class="o">=</span><span class="s1">&#39;custom/cool_new_env-v0&#39;</span><span class="p">,</span>
<span class="n">mp_wrapper</span><span class="o">=</span><span class="n">my_custom_MPWrapper</span>
<span class="p">)</span>
</pre></div>
</div>
<p>If the step-based is not yet registered with gym we can add both the
step-based and MP-versions via</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">fancy_gym</span><span class="o">.</span><span class="n">register</span><span class="p">(</span>
<span class="nb">id</span><span class="o">=</span><span class="s1">&#39;custom/cool_new_env-v0&#39;</span><span class="p">,</span>
<span class="n">entry_point</span><span class="o">=</span><span class="n">my_custom_env</span><span class="p">,</span>
<span class="n">mp_wrapper</span><span class="o">=</span><span class="n">my_custom_MPWrapper</span>
<span class="p">)</span>
</pre></div>
</div>
<p>From this point on, you can access MP-version of your environments via</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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">&#39;custom_ProDMP/cool_new_env-v0&#39;</span><span class="p">)</span>
<span class="n">rewards</span> <span class="o">=</span> <span class="mi">0</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>
<span class="c1"># number of samples/full trajectories (multiple environment steps)</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">5</span><span class="p">):</span>
<span class="n">ac</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="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">ac</span><span class="p">)</span>
<span class="n">rewards</span> <span class="o">+=</span> <span class="n">reward</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="nb">print</span><span class="p">(</span><span class="n">rewards</span><span class="p">)</span>
<span class="n">rewards</span> <span class="o">=</span> <span class="mi">0</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>
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