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<section id="basic-usage">
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<h1>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to this heading"></a></h1>
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<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>
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<section id="step-based-environments">
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<h2>Step-Based Environments<a class="headerlink" href="#step-based-environments" title="Permalink to this heading"></a></h2>
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<p>Regular step based environments added by Fancy Gym are added into the
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<code class="docutils literal notranslate"><span class="pre">fancy/</span></code> namespace.</p>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<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>
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</div>
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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/Reacher5d-v0'</span><span class="p">)</span>
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<span class="c1"># or env = gym.make('metaworld/reach-v2') # fancy_gym allows access to all metaworld ML1 tasks via the metaworld/ NS</span>
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<span class="c1"># or env = gym.make('dm_control/ball_in_cup-catch-v0')</span>
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<span class="c1"># or env = gym.make('Reacher-v2')</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><span class="n">seed</span><span class="o">=</span><span class="mi">1</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>
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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="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>
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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">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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</section>
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<section id="black-box-environments">
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<h2>Black-Box Environments<a class="headerlink" href="#black-box-environments" title="Permalink to this heading"></a></h2>
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<p>All environments provide by default the cumulative episode reward, this
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can however be changed if necessary. Optionally, each environment
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returns all collected information from each step as part of the infos.
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This information is, however, mainly meant for debugging as well as
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logging and not for training.</p>
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<table class="docutils align-default">
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<thead>
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<tr class="row-odd"><th class="head"><p>Key</p></th>
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<th class="head"><p>Description</p></th>
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<th class="head"><p>Type</p></th>
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</tr>
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</thead>
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<tbody>
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<tr class="row-even"><td><p><cite>positions</cite></p></td>
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<td><p>Generated trajectory from MP</p></td>
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<td><p>Optional</p></td>
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</tr>
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<tr class="row-odd"><td><p><cite>velocities</cite></p></td>
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<td><p>Generated trajectory from MP</p></td>
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<td><p>Optional</p></td>
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</tr>
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<tr class="row-even"><td><p><cite>step_actions</cite></p></td>
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<td><p>Step-wise executed action based on controller output</p></td>
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<td><p>Optional</p></td>
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</tr>
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<tr class="row-odd"><td><p><cite>step_observations</cite></p></td>
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<td><p>Step-wise intermediate observations</p></td>
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<td><p>Optional</p></td>
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</tr>
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<tr class="row-even"><td><p><cite>step_rewards</cite></p></td>
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<td><p>Step-wise rewards</p></td>
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<td><p>Optional</p></td>
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</tr>
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<tr class="row-odd"><td><p><cite>trajectory_length</cite></p></td>
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<td><p>Total number of environment interactions</p></td>
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<td><p>Always</p></td>
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</tr>
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<tr class="row-even"><td><p><cite>other</cite></p></td>
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<td><p>All other information from the underlying environment are returned as a list with length <cite>trajectory_length</cite> maintaining the original key.
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In case some information are not provided every time step, the missing values are filled with <cite>None</cite>.</p></td>
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<td><p>Always</p></td>
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</tr>
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</tbody>
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</table>
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<p>Existing MP tasks can be created the same way as above. The namespace of
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a MP-variant of an environment is given by
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<code class="docutils literal notranslate"><span class="pre"><original</span> <span class="pre">namespace>_<MP</span> <span class="pre">name>/</span></code>. Just keep in mind, calling
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<code class="docutils literal notranslate"><span class="pre">step()</span></code> executes a full trajectory.</p>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>Currently, we are also in the process of enabling replanning as
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well as learning of sub-trajectories. This allows to split the
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episode into multiple trajectories and is a hybrid setting between
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step-based and black-box leaning. While this is already
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implemented, it is still in beta and requires further testing. Feel
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free to try it and open an issue with any problems that occur.</p>
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</div>
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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/Reacher5d-v0'</span><span class="p">)</span>
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<span class="c1"># or env = gym.make('metaworld_ProDMP/reach-v2')</span>
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<span class="c1"># or env = gym.make('dm_control_DMP/ball_in_cup-catch-v0')</span>
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<span class="c1"># or env = gym.make('gym_ProMP/Reacher-v2') # mp versions of envs added directly by gymnasium are in the gym_<MP-type> NS</span>
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<span class="c1"># render() can be called once in the beginning with all necessary arguments.</span>
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<span class="c1"># To turn it of again just call render() without any arguments.</span>
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<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">'human'</span><span class="p">)</span>
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<span class="c1"># This returns the context information, not the full state observation</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><span class="n">seed</span><span class="o">=</span><span class="mi">1</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">5</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>
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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="c1"># terminated or truncated is always True as we are working on the episode level, hence we always reset()</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>To show all available environments, we provide some additional
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convenience variables. All of them return a dictionary with the keys
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<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
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available environment ids.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">fancy_gym</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"All Black-box tasks:"</span><span class="p">)</span>
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<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>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"Fancy Black-box tasks:"</span><span class="p">)</span>
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<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>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"OpenAI Gym Black-box tasks:"</span><span class="p">)</span>
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<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>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"Deepmind Control Black-box tasks:"</span><span class="p">)</span>
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<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>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"MetaWorld Black-box tasks:"</span><span class="p">)</span>
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<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>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"If you add custom envs, their mp versions will be found in:"</span><span class="p">)</span>
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<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">'<my_custom_namespace>'</span><span class="p">])</span>
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</pre></div>
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</div>
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</body>
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