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@ -1312,6 +1066,8 @@
<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>
<hr />
<p>For a step-by-step walkthrough on using these evaluation phases to reproduce our project results, see the <strong><a href="../reproduction/">Results &amp; Reproduction Guide</a></strong>.</p>

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<h1 id="simulation-evaluation">Simulation &amp; Evaluation</h1>
<p>The simulation pipeline allows you to visualize trained models and evaluate their performance under various conditions.</p>
<h1 id="interactive-simulation-visualization">Interactive Simulation &amp; Visualization</h1>
<p>The simulation pipeline allows you to visualize trained models and observe their behavior under various conditions.</p>
<h2 id="overview">Overview</h2>
<p>The simulation pipeline is metadata-driven. Training-specific configurations (morphology, arena, environment, etc.) are automatically loaded from the <code>_metadata.yaml</code> file associated with the model checkpoint.</p>
<h2 id="basic-simulation">Basic Simulation</h2>
@ -1220,7 +974,7 @@
</code></pre></div>
<p>Videos and evaluation metadata are stored in timestamped folders alongside the model:
<code>runs/your_run/final_model_evaluations/eval_&lt;timestamp&gt;/simulation.mp4</code></p>
<p>For batch evaluation and cross-model comparison, see the <strong><a href="../evaluation/">Evaluation Guide</a></strong>.</p>
<p>For batch evaluation, checkpoint analysis, and cross-model architecture comparisons, see the <strong><a href="../evaluation/">Checkpoint &amp; Model Evaluation Guide</a></strong>.</p>

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@ -1269,6 +1023,7 @@
<p>This guide covers how to configure and run training experiments for the Brittle Star project using Hydra-based configurations.</p>
<h2 id="configuration">Configuration</h2>
<p>The project uses a modular configuration system powered by <a href="https://hydra.cc/">Hydra</a>. Instead of passing many command-line flags, you select and override configuration groups.</p>
<p>For a detailed guide on the structure, validation, and usage of our Hydra configuration files, see the <strong><a href="../../configs/">Brittle Star Configuration System Guide</a></strong>.</p>
<h3 id="creating-a-custom-experiment">Creating a Custom Experiment</h3>
<ol>
<li>
@ -1301,31 +1056,9 @@
<div class="highlight"><pre><span></span><code>uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/train.py<span class="w"> </span>evaluation.evaluate_checkpoints<span class="o">=</span><span class="nb">true</span>
</code></pre></div>
<h2 id="reproducing-experiments">Reproducing Experiments</h2>
<p>To ensure scientific validity and allow other researchers to reproduce your training runs, follow these steps:</p>
<ol>
<li>
<p><strong>Lock Environment Dependencies</strong>:
Always use the exact environment lockfile when running experiments. Run:
<div class="highlight"><pre><span></span><code>uv<span class="w"> </span>sync<span class="w"> </span>--frozen
</code></pre></div>
This guarantees that the same package versions (including JAX, Flax, and MuJoCo) are used.</p>
</li>
<li>
<p><strong>Save and Locate Configuration Metadata</strong>:
Every time you start a training run, the configuration is fully resolved by Hydra and saved as a metadata YAML file:</p>
</li>
<li>For checkpointed steps: <code>runs/&lt;run_dir&gt;/checkpoints/&lt;prefix&gt;_step_&lt;step&gt;_metadata.yaml</code></li>
<li>For the final model: <code>runs/&lt;run_dir&gt;/final_model_metadata.yaml</code></li>
</ol>
<p>This metadata file contains every active hyperparameter (e.g., learning rate, morphology configuration, PPO parameters, etc.) for that specific run.</p>
<ol>
<li><strong>Re-Run with Pinning</strong>:
To reproduce a run, execute the training script with the configuration parameters specified in the metadata file, making sure to reuse the same seed:
<div class="highlight"><pre><span></span><code>uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/train.py<span class="w"> </span><span class="nv">experiment</span><span class="o">=</span>my_experiment<span class="w"> </span>ppo.learning_rate<span class="o">=</span><span class="m">0</span>.001<span class="w"> </span>experiment.seed<span class="o">=</span><span class="m">42</span>
</code></pre></div></li>
</ol>
<p>For detailed steps on how to reproduce training runs, locate run configuration metadata, or reproduce our experiments using Weights &amp; Biases (WandB), see the <strong><a href="../reproduction/">Results &amp; Reproduction Guide</a></strong>.</p>
<hr />
<p>For more details on evaluation metrics and comparison tools, see <a href="../evaluation/">Evaluation</a>.</p>
<p>For more details on evaluation metrics and comparison tools, see <a href="../evaluation/">Checkpoint &amp; Model Evaluation</a>.</p>
<p>For more details on tracking your experiments, see <a href="../tracking/">Tracking &amp; Monitoring</a>.</p>