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Gym-MiniGrid
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  <p class="caption" role="heading"><span class="caption-text">Introduction</span></p>
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            <section class="tex2jax_ignore mathjax_ignore" id="training-minigrid-environments">
<h1>Training Minigrid Environments<a class="headerlink" href="#training-minigrid-environments" title="Link to this heading">#</a></h1>
<p>The environments in the Minigrid library can be trained easily using <a class="reference external" href="https://stable-baselines3.readthedocs.io/en/master/">StableBaselines3</a>. In this tutorial we show how a PPO agent can be trained on the <code class="docutils literal notranslate"><span class="pre">MiniGrid-Empty-16x16-v0</span></code> environment.</p>
<section id="create-custom-feature-extractor">
<h2>Create Custom Feature Extractor<a class="headerlink" href="#create-custom-feature-extractor" title="Link to this heading">#</a></h2>
<p>Although <code class="docutils literal notranslate"><span class="pre">StableBaselines3</span></code> is fully compatible with <code class="docutils literal notranslate"><span class="pre">Gymnasium</span></code>-based environments (which includes Minigrid), the default CNN architecture does not directly support the Minigrid observation space. Thus, to train an agent on Minigrid environments, we need to create a custom feature extractor. This can be done by creating a feature extractor class that inherits from <code class="docutils literal notranslate"><span class="pre">stable_baselines3.common.torch_layers.BaseFeaturesExtractor</span></code></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MinigridFeaturesExtractor</span><span class="p">(</span><span class="n">BaseFeaturesExtractor</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">observation_space</span><span class="p">:</span> <span class="n">gym</span><span class="o">.</span><span class="n">Space</span><span class="p">,</span> <span class="n">features_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">512</span><span class="p">,</span> <span class="n">normalized_image</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">observation_space</span><span class="p">,</span> <span class="n">features_dim</span><span class="p">)</span>
        <span class="n">n_input_channels</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="bp">self</span><span class="o">.</span><span class="n">cnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">n_input_channels</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(),</span>
        <span class="p">)</span>

        <span class="c1"># Compute shape by doing one forward pass</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
            <span class="n">n_flatten</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cnn</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">observation_space</span><span class="o">.</span><span class="n">sample</span><span class="p">()[</span><span class="kc">None</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">())</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">n_flatten</span><span class="p">,</span> <span class="n">features_dim</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">())</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">observations</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cnn</span><span class="p">(</span><span class="n">observations</span><span class="p">))</span>
</pre></div>
</div>
<p>This class is created based on the custom feature extractor <a class="reference external" href="https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html#custom-feature-extractor:~:text=Custom%20Feature%20Extractor-,%EF%83%81,-If%20you%20want">documentation</a>, the CNN architecture is copied from Lucas Willems’ <a class="reference external" href="https://github.com/lcswillems/rl-starter-files/blob/317da04a9a6fb26506bbd7f6c7c7e10fc0de86e0/model.py#L18">rl-starter-files</a>.</p>
</section>
<section id="train-a-ppo-agent">
<h2>Train a PPO Agent<a class="headerlink" href="#train-a-ppo-agent" title="Link to this heading">#</a></h2>
<p>The using the custom feature extractor, we can train a PPO agent on the <code class="docutils literal notranslate"><span class="pre">MiniGrid-Empty-16x16-v0</span></code> environment. The following code snippet shows how this can be done.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">minigrid</span>
<span class="kn">from</span> <span class="nn">minigrid.wrappers</span> <span class="kn">import</span> <span class="n">ImgObsWrapper</span>
<span class="kn">from</span> <span class="nn">stable_baselines3</span> <span class="kn">import</span> <span class="n">PPO</span>

<span class="n">policy_kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
    <span class="n">features_extractor_class</span><span class="o">=</span><span class="n">MinigridFeaturesExtractor</span><span class="p">,</span>
    <span class="n">features_extractor_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">features_dim</span><span class="o">=</span><span class="mi">128</span><span class="p">),</span>
<span class="p">)</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="s2">&quot;MiniGrid-Empty-16x16-v0&quot;</span><span class="p">,</span> <span class="n">render_mode</span><span class="o">=</span><span class="s2">&quot;rgb_array&quot;</span><span class="p">)</span>
<span class="n">env</span> <span class="o">=</span> <span class="n">ImgObsWrapper</span><span class="p">(</span><span class="n">env</span><span class="p">)</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">PPO</span><span class="p">(</span><span class="s2">&quot;CnnPolicy&quot;</span><span class="p">,</span> <span class="n">env</span><span class="p">,</span> <span class="n">policy_kwargs</span><span class="o">=</span><span class="n">policy_kwargs</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">learn</span><span class="p">(</span><span class="mf">2e5</span><span class="p">)</span>
</pre></div>
</div>
<p>By default the observation of Minigrid environments are dictionaries. Since the <code class="docutils literal notranslate"><span class="pre">CnnPolicy</span></code> from StableBaseline3 by default takes in image observations, we need to wrap the environment using the <code class="docutils literal notranslate"><span class="pre">ImgObsWrapper</span></code> from the Minigrid library. This wrapper converts the dictionary observation to an image observation.</p>
</section>
<section id="further-reading">
<h2>Further Reading<a class="headerlink" href="#further-reading" title="Link to this heading">#</a></h2>
<p>One can also pass dictionary observations to StableBaseline3 policies, for a walkthrough the process of doing so see <a class="reference external" href="https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html#multiple-inputs-and-dictionary-observations">here</a>. An implementation utilizing this functionality can be found <a class="reference external" href="https://github.com/BolunDai0216/MinigridMiniworldTransfer/blob/main/minigrid_gotoobj_train.py">here</a>.</p>
</section>
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          if (projectJson.website !== null) {
            projectJson.link = projectJson.website;
          } else {
            projectJson.link = projectJson.github;
          }
          if (projectJson.type === "core") {
            sections["Core Projects"].push(projectJson)
          } else if (projectJson.type == "mature") {
            if (projectJson.website !== null) {
              sections["Mature Projects"]["Documentation"].push(projectJson)
            } else {
              sections["Mature Projects"]["Repositories"].push(projectJson)
            }
          } else {
            if (projectJson.website !== null) {
              sections["Incubating Projects"]["Documentation"].push(projectJson)
            } else {
              sections["Incubating Projects"]["Repositories"].push(projectJson)
            }
          }
        })

        const menuContainer = document.querySelector(".farama-header-menu__body");

        Object.keys(sections).forEach((key, i) => {
          const sectionElem = Object.assign(
            document.createElement('div'), {
              className:'farama-header-menu__section',
            }
          )
          sectionElem.appendChild(Object.assign(document.createElement('span'),
            {
              className:'farama-header-menu__section-title' ,
              innerText: key
            }
          ))
          // is not a list
          if (sections[key].constructor !== Array) {
            const subSections = sections[key];
            const subSectionContainerElem = Object.assign(
                document.createElement('div'), {
                  className:'farama-header-menu__subsections-container',
                  style: 'display: flex'
                }
            )
            Object.keys(subSections).forEach((subKey, i) => {
              const subSectionElem = Object.assign(
                document.createElement('div'), {
                  className:'farama-header-menu__subsection',
                }
              )
              subSectionElem.appendChild(Object.assign(document.createElement('span'),
                {
                  className:'farama-header-menu__subsection-title' ,
                  innerText: subKey
                }
              ))
              const ulElem = createProjectsList(subSections[subKey], key !== 'Foundation');
              subSectionElem.appendChild(ulElem);
              subSectionContainerElem.appendChild(subSectionElem);
            })
            sectionElem.appendChild(subSectionContainerElem);
          } else {
            const projects = sections[key];
            const ulElem = createProjectsList(projects, true);
            sectionElem.appendChild(ulElem);
          }
          menuContainer.appendChild(sectionElem)
        });
      }

      xhr.onerror = function() {
        console.error("Unable to load projects");
      };

      xhr.send();
    </script>

    
    <script>
      const versioningConfig = {
        githubUser: 'Farama-Foundation',
        githubRepo: 'Minigrid',
      };
      fetch('/main/_static/versioning/versioning_menu.html').then(response => {
        if (response.status === 200) {
            response.text().then(text => {
                const container = document.createElement("div");
                container.innerHTML = text;
                document.querySelector("body").appendChild(container);
                // innerHtml doenst evaluate scripts, we need to add them dynamically
                Array.from(container.querySelectorAll("script")).forEach(oldScript => {
                    const newScript = document.createElement("script");
                    Array.from(oldScript.attributes).forEach(attr => newScript.setAttribute(attr.name, attr.value));
                    newScript.appendChild(document.createTextNode(oldScript.innerHTML));
                    oldScript.parentNode.replaceChild(newScript, oldScript);
                });
            });
        } else {
            console.warn("Unable to load versioning menu", response);
        }
      });
    </script>

    </body>
</html>