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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">'ProMP'</span><span class="p">:</span> <span class="p">{},</span>
<span class="s1">'DMP'</span><span class="p">:</span> <span class="p">{},</span>
<span class="s1">'ProDMP'</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">-></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">"""</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"> """</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">-></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">"""</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"> """</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">-></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">"""</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"> """</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">'ProMP'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'phase_generator_kwargs'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'phase_generator_type'</span><span class="p">:</span> <span class="s1">'linear'</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">'controller_kwargs'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'p_gains'</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">'d_gains'</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">'basis_generator_kwargs'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'num_basis'</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
<span class="s1">'num_basis_zero_start'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s1">'num_basis_zero_goal'</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">'DMP'</span><span class="p">:</span> <span class="p">{},</span>
<span class="s1">'ProDMP'</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">'custom/cool_new_env-v0'</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">'custom/cool_new_env-v0'</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">'custom_ProDMP/cool_new_env-v0'</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>
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