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CONTRIBUTING/index.html

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DEVELOPMENT/index.html

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HPC/index.html

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api/analysis/index.html

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api/environment/index.html

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api/evaluation/index.html

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api/simulation/index.html

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api/tracking/index.html

@@ -1267,7 +1267,7 @@
 <p>All runs are recorded locally in the <code>runs/</code> directory (or the directory specified in <code>experiment.base_run_dir</code>). You can view scalars and other metrics with TensorBoard:</p>
 <div class="highlight"><pre><span></span><code>tensorboard<span class="w"> </span>--logdir<span class="w"> </span>runs/
 </code></pre></div>
-<p>Access the interface at <code>http://localhost:6006</code>.</p>
+<p>Access the interface at <a href="http://localhost:6006">http://localhost:6006</a>.</p>
 <h3 id="cli-exploration-tool">CLI Exploration Tool</h3>
 <p>For quick diagnostics or to export data to CSV without launching the full TensorBoard UI, you can use the <code>explore_tensorboard.py</code> script:</p>
 <div class="highlight"><pre><span></span><code>uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/analysis/explore_tensorboard.py<span class="w"> </span>runs/your_run_name/
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api/training/index.html

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design/actor-critic/index.html

@@ -1136,12 +1136,12 @@ estimation (Critic).</p>
 <p><strong>Centralized Architecture (Baseline)</strong></p>
 <p>This pipeline treats the agent as a single entity and uses standard Proximal Policy Optimization (PPO).</p>
 <ul>
-<li>Centralized Actor: Composed of two chained MLPs (Sensor $\rightarrow$ Motor) passing a hidden state between them. The
+<li>Centralized Actor: Composed of two chained MLPs (Sensor <span class="arithmatex">\(\rightarrow\)</span> Motor) passing a hidden state between them. The
   centralized sensor receives the concatenated global state vector of all limbs at once and processes it into a hidden
   state. The centralized motor receives this hidden state and outputs the joint offsets for all actuators
   simultaneously. This is mathematically equivalent to using one large MLP with hidden layers, but splitting makes the
   implementation easier by allowing us to reuse the same components for the decentralized modules.</li>
-<li>Centralized Critic: Composed of two sequential MLPs (Feature Extractor $\rightarrow$ Critic). Because PPO evaluates
+<li>Centralized Critic: Composed of two sequential MLPs (Feature Extractor <span class="arithmatex">\(\rightarrow\)</span> Critic). Because PPO evaluates
   the state-value function, this network only receives the concatenated global state vector (no actions). It outputs a
   single scalar estimating the expected future reward for the entire agent.</li>
 </ul>
@@ -1172,10 +1172,10 @@ variant.</p>
 <li>Decentralized Actor, split into three distinct models:</li>
 <li>Sensor: A local model at each node. It receives its local state plus the goal vector directly, processing them into
     an initial hidden state.</li>
-<li>Propagator: Nodes synchronously compute and exchange messages with connected neighbors for $N$ steps to update
+<li>Propagator: Nodes synchronously compute and exchange messages with connected neighbors for <span class="arithmatex">\(N\)</span> steps to update
     their hidden states. See <a href="../communication/">communication.md</a> for details.</li>
 <li>Motor: A local model uses its final updated hidden state to output the joint offset strictly for its own actuator.</li>
-<li>Centralized Critic: Composed of two sequential MLPs (Feature Extractor $\rightarrow$ Critic). During training, it
+<li>Centralized Critic: Composed of two sequential MLPs (Feature Extractor <span class="arithmatex">\(\rightarrow\)</span> Critic). During training, it
   acts globally by taking the concatenated state vectors from all sensors to output a single, global state-value scalar
   evaluating the entire agent's pose.</li>
 </ul>
@@ -1217,7 +1217,7 @@ critic for all nodes at once, for the following reasons:</p>
 
     Prop -.-&gt;|"message passing"|Prop</code></pre>
 <h2 id="implementation-details-network-depth">Implementation Details (Network Depth)</h2>
-<p>Inspired by: https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/</p>
+<p>Inspired by: <a href="https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/">PPO Implementation Details</a></p>
 <p>The MLPs used in both pipelines are defined with specific hidden layer configurations to balance learning capability
 and computational cost. As of right now, though this might change as we make progress in our experiments, we use:</p>
 <ul>
@@ -1233,9 +1233,9 @@ and computational cost. As of right now, though this might change as we make pro
 by previous research to maintain learning stability.</p>
 <p><strong>References</strong></p>
 <ul>
-<li>Ha, D. (2017, October 29). A Visual Guide to Evolution Strategies. 大トロ ・ Machine Learning. https://blog.otoro.net/2017/10/29/visual-evolution-strategies/</li>
-<li>Schulman, John, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. ‘Proximal Policy Optimization Algorithms’. arXiv:1707.06347. Preprint, arXiv, 28 August 2017. https://doi.org/10.48550/arXiv.1707.06347.</li>
-<li>Wang, Tingwu, Renjie Liao, Jimmy Ba, and S. Fidler. ‘NerveNet: Learning Structured Policy with Graph Neural Networks’. Conference paper presented at International Conference on Learning Representations. 15 February 2018. https://www.semanticscholar.org/paper/NerveNet:-Learning-Structured-Policy-with-Graph-Wang-Liao/249408527106d7595d45dd761dd53c83e5a02613.</li>
+<li>Ha, D. (2017, October 29). A Visual Guide to Evolution Strategies. 大トロ ・ Machine Learning. <a href="https://blog.otoro.net/2017/10/29/visual-evolution-strategies/">https://blog.otoro.net/2017/10/29/visual-evolution-strategies/</a></li>
+<li>Schulman, John, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. ‘Proximal Policy Optimization Algorithms’. arXiv:1707.06347. Preprint, arXiv, 28 August 2017. <a href="https://doi.org/10.48550/arXiv.1707.06347">https://doi.org/10.48550/arXiv.1707.06347</a>.</li>
+<li>Wang, Tingwu, Renjie Liao, Jimmy Ba, and S. Fidler. ‘NerveNet: Learning Structured Policy with Graph Neural Networks’. Conference paper presented at International Conference on Learning Representations. 15 February 2018. <a href="https://www.semanticscholar.org/paper/NerveNet:-Learning-Structured-Policy-with-Graph-Wang-Liao/249408527106d7595d45dd761dd53c83e5a02613">https://www.semanticscholar.org/paper/NerveNet:-Learning-Structured-Policy-with-Graph-Wang-Liao/249408527106d7595d45dd761dd53c83e5a02613</a>.</li>
 </ul>
 
 
@@ -1291,6 +1291,10 @@ by previous research to maintain learning stability.</p>
     
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design/communication/index.html

@@ -1183,8 +1183,8 @@ two passes. For very large or long morphologies, this would be much more scalabl
 extended morphologies.</p>
 <p><strong>References</strong></p>
 <ul>
-<li>Wang, Tingwu, Renjie Liao, Jimmy Ba, and S. Fidler. ‘NerveNet: Learning Structured Policy with Graph Neural Networks’. Conference paper presented at International Conference on Learning Representations. 15 February 2018. https://www.semanticscholar.org/paper/NerveNet:-Learning-Structured-Policy-with-Graph-Wang-Liao/249408527106d7595d45dd761dd53c83e5a02613.</li>
-<li>Huang, Wenlong, Igor Mordatch, and Deepak Pathak. ‘One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control’. arXiv:2007.04976. Preprint, arXiv, 9 July 2020. https://doi.org/10.48550/arXiv.2007.04976.</li>
+<li>Wang, Tingwu, Renjie Liao, Jimmy Ba, and S. Fidler. ‘NerveNet: Learning Structured Policy with Graph Neural Networks’. Conference paper presented at International Conference on Learning Representations. 15 February 2018. <a href="https://www.semanticscholar.org/paper/NerveNet:-Learning-Structured-Policy-with-Graph-Wang-Liao/249408527106d7595d45dd761dd53c83e5a02613">https://www.semanticscholar.org/paper/NerveNet:-Learning-Structured-Policy-with-Graph-Wang-Liao/249408527106d7595d45dd761dd53c83e5a02613</a>.</li>
+<li>Huang, Wenlong, Igor Mordatch, and Deepak Pathak. ‘One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control’. arXiv:2007.04976. Preprint, arXiv, 9 July 2020. <a href="https://doi.org/10.48550/arXiv.2007.04976">https://doi.org/10.48550/arXiv.2007.04976</a>.</li>
 </ul>
 
 
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design/controllers/index.html

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design/input_action_spaces/index.html

@@ -1205,11 +1205,11 @@ decentralized control architecture in mind, we divide these inputs into global a
 <ul>
 <li>Vertical orientation/tilt: A single, simplified metric representing the tilt/vertical alignment of the agent's
   central body/disk, a.k.a. the deviation from the global Z-axis. Its value is derived from the environment's raw disk
-  rotation 3D vector $[roll, pitch, yaw]$:
+  rotation 3D vector <span class="arithmatex">\([roll, pitch, yaw]\)</span>:
   $$
   tilt = sqrt(roll^2 + pitch^2)
   $$</li>
-<li>Goal vector: A 2D unit vector representing the <em>egocentric</em> direction to the target. A value of $[1.0, 0.0]$
+<li>Goal vector: A 2D unit vector representing the <em>egocentric</em> direction to the target. A value of <span class="arithmatex">\([1.0, 0.0]\)</span>
   indicates that the target is directly in front of the agent (angle 0).</li>
 </ul>
 <p>Local inputs, routed directly to specific nodes:</p>
@@ -1225,10 +1225,10 @@ decentralized control architecture in mind, we divide these inputs into global a
 <li>Joint offsets: <em>absolute</em> target positions (offsets) for the joints, i.e. the exact angle the joint should move to.</li>
 </ul>
 <h2 id="normalization-and-scaling">Normalization and Scaling</h2>
-<p>Both the input (observation) and output (action) spaces are rescaled to the range <strong>$[-1, 1]$</strong>. </p>
+<p>Both the input (observation) and output (action) spaces are rescaled to the range <strong><span class="arithmatex">\([-1, 1]\)</span></strong>. </p>
 <p>For the input space, all raw physical values (angles, velocities, forces, distances) are normalized based on their
-defined physical bounds. If a value exceeds these bounds during simulation, it is clipped to the $[-1, 1]$ range.</p>
-<p>For the output space, the neural network's tanh-activated outputs (which naturally fall in $[-1, 1]$) are linearly
+defined physical bounds. If a value exceeds these bounds during simulation, it is clipped to the <span class="arithmatex">\([-1, 1]\)</span> range.</p>
+<p>For the output space, the neural network's tanh-activated outputs (which naturally fall in <span class="arithmatex">\([-1, 1]\)</span>) are linearly
 mapped to the physical joint limits defined in the robot's morphology.</p>
 <h2 id="rationale">Rationale</h2>
 <p>When designing the state space, we must ask: <em>Could a human operator perform this task given only these inputs?</em></p>
@@ -1254,7 +1254,7 @@ mapped to the physical joint limits defined in the robot's morphology.</p>
   exactly where the target is relative to its current orientation, allowing for directed and efficient locomotion.</li>
 </ul>
 <p>The goal representation is explicitly divided into a directional vector and a scalar distance. Keeping the direction
-  as a normalized unit vector bounds the values to the $[-1, 1]$ range, which stabilizes neural network training.
+  as a normalized unit vector bounds the values to the <span class="arithmatex">\([-1, 1]\)</span> range, which stabilizes neural network training.
   Providing only a scalar "distance to the goal" would force the agent to learning localized searching behaviors (e.g.
   random walks or spiraling) to deduce the direction, drastically increasing the difficulty of the learning task.</p>
 <p><strong>NOTE:</strong> We later dropped the "distance to vector", switching to only a direction as the input. Our reasoning is
@@ -1262,18 +1262,18 @@ mapped to the physical joint limits defined in the robot's morphology.</p>
   simplification that decreases the model input size.</p>
 <p>The environment provides a raw <code>unit_xy_direction_to_target</code> (global), which we transform into a calculated
   <code>robot_direction_to_target</code> (egocentric) before passing it to the MLPs. This vector consists of the X and Y
-  direction, where a value of $[1.0, 0.0]$ (mapping to an angle of $0$) means the robot is facing directly towards the
+  direction, where a value of <span class="arithmatex">\([1.0, 0.0]\)</span> (mapping to an angle of <span class="arithmatex">\(0\)</span>) means the robot is facing directly towards the
   target.
 - Contact sensors: Segment contact detects external ground interaction and is biologically vital for timing gait
   transitions.
-- Zero-Centered Rescaling ($[-1, 1]$): Using a zero-centered range is standard best practice for continuous control
+- Zero-Centered Rescaling (<span class="arithmatex">\([-1, 1]\)</span>): Using a zero-centered range is standard best practice for continuous control
   tasks. It provides several mathematical and physical advantages:
   - Improved Gradient Flow: Neural networks optimize faster when inputs are zero-centered. If all inputs were positive
-    (e.g., $[0, 1]$), the gradients during backpropagation would be forced to the same sign, causing inefficient
+    (e.g., <span class="arithmatex">\([0, 1]\)</span>), the gradients during backpropagation would be forced to the same sign, causing inefficient
     "zig-zag" weight updates.
-  - Meaningful Neutral State: In robotics, $0.0$ naturally represents a resting state (zero velocity, centered
-    position, no force). In a $[-1, 1]$ system, this physical rest maps to a neutral $0.0$ signal in the network.
-    This also correctly communicaties a "neutral/dead" signal for amputated limbs that are padded with $0.0$ values.</p>
+  - Meaningful Neutral State: In robotics, <span class="arithmatex">\(0.0\)</span> naturally represents a resting state (zero velocity, centered
+    position, no force). In a <span class="arithmatex">\([-1, 1]\)</span> system, this physical rest maps to a neutral <span class="arithmatex">\(0.0\)</span> signal in the network.
+    This also correctly communicaties a "neutral/dead" signal for amputated limbs that are padded with <span class="arithmatex">\(0.0\)</span> values.</p>
 <p>Specifically, we do not include some available inputs:</p>
 <ul>
 <li>Global position: Absolute spatial coordinates can cause the agent to overfit to a specific coordinate frame or map,
@@ -1292,13 +1292,13 @@ mapped to the physical joint limits defined in the robot's morphology.</p>
 <li>Scalar Goal Distance: Giving the agent only the scalar distance to the target would force it to learn a localized
   searching behavior (e.g., spiraling or random walks) to determine the correct direction. While biologically plausible
   for simpler organisms following chemical gradients, it drastically increases the difficulty of the learning task.</li>
-<li><strong>$[0, 1]$ Rescaling:</strong> While some domains (like computer vision) use $[0, 1]$ scaling, it is generally avoided in
-  robotics. Scaling to $[0, 1]$ would mean that a resting joint (velocity = 0) maps to an input of $0.5$. This
+<li><strong><span class="arithmatex">\([0, 1]\)</span> Rescaling:</strong> While some domains (like computer vision) use <span class="arithmatex">\([0, 1]\)</span> scaling, it is generally avoided in
+  robotics. Scaling to <span class="arithmatex">\([0, 1]\)</span> would mean that a resting joint (velocity = 0) maps to an input of <span class="arithmatex">\(0.5\)</span>. This
   constant positive bias forces the network to waste capacity learning to ignore or subtract this baseline signal just
   to stand still. Furthermore, it breaks the "dead signal" interpretation of zero-padding used for amputations.</li>
 </ul>
 <h2 id="mujoco">MuJoCo</h2>
-<p>This is what the filtered input vectors look like in MuJoCo, with $J$ joints and $S$ segments:</p>
+<p>This is what the filtered input vectors look like in MuJoCo, with <span class="arithmatex">\(J\)</span> joints and <span class="arithmatex">\(S\)</span> segments:</p>
 <ul>
 <li><code>joint_position</code>: shape=(J,), dtype=float64</li>
 <li><code>joint_velocity</code>: shape=(J,), dtype=float64</li>
@@ -1307,7 +1307,7 @@ mapped to the physical joint limits defined in the robot's morphology.</p>
 <li><code>robot_direction_to_target</code>: shape=(2,), dtype=float64, egocentric</li>
 <li><code>disk_z_tilt</code>: shape=(1,), dtype=float64, derived from <code>disk_rotation</code></li>
 </ul>
-<p>This brings the entire input space down to $3J + S + 4$ float64's, compared to $4J + S + 15$ float64's for the
+<p>This brings the entire input space down to <span class="arithmatex">\(3J + S + 4\)</span> float64's, compared to <span class="arithmatex">\(4J + S + 15\)</span> float64's for the
 unfiltered inputs.</p>
 <p>For reference, these are all the inputs that are available in the MuJoCo environment:</p>
 <div class="highlight"><pre><span></span><code>obs keys: [&#39;joint_position&#39;, &#39;joint_velocity&#39;, &#39;joint_actuator_force&#39;, &#39;actuator_force&#39;, &#39;disk_position&#39;, &#39;disk_rotation&#39;, &#39;disk_linear_velocity&#39;, &#39;disk_angular_velocity&#39;, &#39;tendon_position&#39;, &#39;tendon_velocity&#39;, &#39;segment_contact&#39;, &#39;unit_xy_direction_to_target&#39;, &#39;xy_distance_to_target&#39;]
@@ -1395,6 +1395,10 @@ xy_distance_to_target: shape=(1,), dtype=float64, size=1
     
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design/learning_algorithm/index.html

@@ -1172,9 +1172,9 @@ failures.</p>
 </ul>
 <p><strong>References</strong></p>
 <ul>
-<li>Fujimoto, Scott, Herke Hoof, and David Meger. ‘Addressing Function Approximation Error in Actor-Critic Methods’. Proceedings of the 35th International Conference on Machine Learning, 3 July 2018, 1587-96. https://proceedings.mlr.press/v80/fujimoto18a.html.</li>
-<li>Huang, Wenlong, Igor Mordatch, and Deepak Pathak. ‘One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control’. arXiv:2007.04976. Preprint, arXiv, 9 July 2020. https://doi.org/10.48550/arXiv.2007.04976.</li>
-<li>Schulman, John, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. ‘Proximal Policy Optimization Algorithms’. arXiv:1707.06347. Preprint, arXiv, 28 August 2017. https://doi.org/10.48550/arXiv.1707.06347.</li>
+<li>Fujimoto, Scott, Herke Hoof, and David Meger. ‘Addressing Function Approximation Error in Actor-Critic Methods’. Proceedings of the 35th International Conference on Machine Learning, 3 July 2018, 1587-96. <a href="https://proceedings.mlr.press/v80/fujimoto18a.html">https://proceedings.mlr.press/v80/fujimoto18a.html</a>.</li>
+<li>Huang, Wenlong, Igor Mordatch, and Deepak Pathak. ‘One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control’. arXiv:2007.04976. Preprint, arXiv, 9 July 2020. <a href="https://doi.org/10.48550/arXiv.2007.04976">https://doi.org/10.48550/arXiv.2007.04976</a>.</li>
+<li>Schulman, John, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. ‘Proximal Policy Optimization Algorithms’. arXiv:1707.06347. Preprint, arXiv, 28 August 2017. <a href="https://doi.org/10.48550/arXiv.1707.06347">https://doi.org/10.48550/arXiv.1707.06347</a>.</li>
 </ul>
 
 
@@ -1230,6 +1230,10 @@ failures.</p>
     
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design/reward_function/index.html

@@ -1180,7 +1180,7 @@ inputs must be distributed fairly to guarantee an objective comparison between d
 <li>The distance from the robot to the target and/or the light intensity are treated as global inputs.</li>
 <li>Positions and joints, which are normalized to floating-point values between 0 and 1, are considered local inputs.</li>
 <li>The reward function is centered around minimizing the distance to the goal or maximizing the movement towards the goal
-  within a finite number of timesteps $T$.</li>
+  within a finite number of timesteps <span class="arithmatex">\(T\)</span>.</li>
 <li>To motivate efficient movement, the amount of timesteps taken to reach the goal will be used as penalty.</li>
 <li>An extra penalty based on movement relative to the current step and the previous is used to penalize a movement away from the target.</li>
 </ul>
@@ -1252,6 +1252,10 @@ this down as potential future research.</p>
     
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index.html

@@ -1148,6 +1148,8 @@
 
 
 <h1 id="documentation">Documentation</h1>
+<p>Welcome to the Brittle Star project documentation. This codebase contains the implementations and research for the scientific evaluation of controller modularity in brittle-star-like robots trained using Reinforcement Learning.</p>
+<p>For the core codebase, scripts, and contribution history, visit our <a href="https://github.com/SELab-3-2026/SEL3-2026-Groep-4">GitHub Repository</a>.</p>
 <h2 id="design-architecture-design">Design &amp; architecture (<code>/design</code>)</h2>
 <p>If you are interested in the "why did you do it like this?"</p>
 <ul>
@@ -1222,6 +1224,10 @@
     
       <script src="assets/javascripts/bundle.79ae519e.min.js"></script>
       
+        <script src="javascripts/mathjax.js"></script>
+      
+        <script src="https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js"></script>
+      
     
   </body>
 </html>

javascripts/mathjax.js

@@ -0,0 +1,18 @@
+window.MathJax = {
+  tex: {
+    inlineMath: [["\\(", "\\)"]],
+    displayMath: [["\\[", "\\]"]],
+    processEscapes: true,
+    processEnvironments: true
+  },
+  options: {
+    ignoreHtmlClass: ".*|",
+    processHtmlClass: "arithmatex"
+  }
+};
+
+document.addEventListener("DOMContentLoaded", () => {
+  MathJax.startup.document.state(0);
+  MathJax.typesetClear();
+  MathJax.typesetPromise();
+});

scripts/analysis/index.html

@@ -1239,6 +1239,10 @@ python<span class="w"> </span>explore_tensorboard.py<span class="w"> </span>&lt;
     
       <script src="../../assets/javascripts/bundle.79ae519e.min.js"></script>
       
+        <script src="../../javascripts/mathjax.js"></script>
+      
+        <script src="https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js"></script>
+      
     
   </body>
 </html>

src/experiment_logger/index.html

@@ -1412,6 +1412,10 @@
     
       <script src="../../assets/javascripts/bundle.79ae519e.min.js"></script>
       
+        <script src="../../javascripts/mathjax.js"></script>
+      
+        <script src="https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js"></script>
+      
     
   </body>
 </html>