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<h1 id="checkpoint-model-evaluation">Checkpoint &amp; Model Evaluation</h1>
<p>This guide covers how to evaluate trained brittle star models, with a focus on measuring defect tolerance (amputations) across different controller architectures.</p>
<h2 id="checkpoint-evaluation-during-training">Checkpoint Evaluation (During Training)</h2>
<p>The <code>PPOTrainer</code> can automatically evaluate every saved checkpoint using the fast MJX backend. This is enabled via configuration.</p>
<h3 id="configuration">Configuration</h3>
<p>In your experiment config or via CLI:
<div class="highlight"><pre><span></span><code>python<span class="w"> </span>scripts/train.py<span class="w"> </span>evaluation.evaluate_checkpoints<span class="o">=</span><span class="nb">true</span><span class="w"> </span>evaluation.eval_max_steps<span class="o">=</span><span class="m">5000</span>
</code></pre></div></p>
<p>Results are saved to <code>runs/&lt;run_dir&gt;/metrics/checkpoint_evaluation.csv</code> and synced to Weights &amp; Biases if enabled.</p>
<h2 id="cross-model-fault-tolerance-analysis">Cross-Model &amp; Fault Tolerance Analysis</h2>
<p>To measure how well different controllers handle damage (amputations), use <code>scripts/compare_models.py</code>. This script performs a grid search over models x morphologies.</p>
<ol>
<li>Create or update a YAML file in <code>configs/evaluation</code>.</li>
<li>Run the benchmark:</li>
</ol>
<div class="highlight"><pre><span></span><code>python<span class="w"> </span>scripts/compare_models.py<span class="w"> </span><span class="nv">evaluation</span><span class="o">=</span>poster
</code></pre></div>
<p>The script will evaluate every combination of model and morphology for the specified number of episodes.</p>
<p>The results are saved to a CSV (default: <code>metrics/model_comparison.csv</code>).</p>
<h3 id="csv-schema">CSV Schema</h3>
<table>
<thead>
<tr>
<th>Column</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>model_path</code></td>
<td>Path to the trained weights.</td>
</tr>
<tr>
<td><code>architecture</code></td>
<td>The <code>morph_mode</code> of the model (e.g., <code>CENTRALIZED</code>, <code>RING</code>).</td>
</tr>
<tr>
<td><code>arm_0</code> ... <code>arm_4</code></td>
<td>Number of segments in each arm slot (0 = amputated).</td>
</tr>
<tr>
<td><code>num_active_arms</code></td>
<td>Total number of arms with segments &gt; 0.</td>
</tr>
<tr>
<td><code>seed</code></td>
<td>The episode seed.</td>
</tr>
<tr>
<td><code>eval_return</code></td>
<td>Accumulated shaped reward.</td>
</tr>
<tr>
<td><code>approx_max_velocity</code></td>
<td>Average velocity: <code>(initial_dist - final_dist) / steps</code>.</td>
</tr>
<tr>
<td><code>reached_target</code></td>
<td>Whether the robot finished within the success radius.</td>
</tr>
</tbody>
</table>
<h2 id="post-hoc-checkpoint-scanning">Post-hoc Checkpoint Scanning</h2>
<p>If you need to re-evaluate every saved checkpoint in a run (e.g., to generate a learning curve with different metrics):</p>
<div class="highlight"><pre><span></span><code>python<span class="w"> </span>scripts/evaluate_checkpoints.py<span class="w"> </span><span class="se">\</span>
<span class="w">    </span>simulation.model_path<span class="o">=</span>runs/&lt;run_id&gt;/final_model.flax<span class="w"> </span><span class="se">\</span>
<span class="w">    </span>evaluation.eval_max_steps<span class="o">=</span><span class="m">2000</span>
</code></pre></div>
<p>This script scans the <code>checkpoints/</code> directory of the specified run and evaluates every <code>.flax</code> file it finds using the model's training morphology.</p>


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