docs: latex and urls

docs/HPC.md

@@ -1,6 +1,6 @@
 # HPC Guide
 
-Full documentation: <https://docs.hpc.ugent.be/>
+Full documentation: [https://docs.hpc.ugent.be/](https://docs.hpc.ugent.be/)
 
 ## Storage Overview
 

docs/README.md

@@ -1,5 +1,9 @@
 # Documentation
 
+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.
+
+For the core codebase, scripts, and contribution history, visit our [GitHub Repository](https://github.com/SELab-3-2026/SEL3-2026-Groep-4).
+
 ## Design & architecture (`/design`)
 
 If you are interested in the "why did you do it like this?"

docs/api/tracking.md

@@ -47,7 +47,7 @@ All runs are recorded locally in the `runs/` directory (or the directory specifi
 tensorboard --logdir runs/
 ```
 
-Access the interface at `http://localhost:6006`.
+Access the interface at [http://localhost:6006](http://localhost:6006).
 
 ### CLI Exploration Tool
 

docs/design/actor-critic.md

@@ -98,7 +98,7 @@ graph TD
 
 ## Implementation Details (Network Depth)
 
-Inspired by: https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/
+Inspired by: [PPO Implementation Details](https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/)
 
 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:
@@ -116,6 +116,6 @@ by previous research to maintain learning stability.
 
 **References**
 
-- 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/
+- 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/](https://blog.otoro.net/2017/10/29/visual-evolution-strategies/)
-- 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.
+- 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](https://doi.org/10.48550/arXiv.1707.06347).
-- 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.
+- 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](https://www.semanticscholar.org/paper/NerveNet:-Learning-Structured-Policy-with-Graph-Wang-Liao/249408527106d7595d45dd761dd53c83e5a02613).

docs/design/communication.md

@@ -40,5 +40,5 @@ extended morphologies.
 
 **References**
 
-- 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.
+- 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](https://www.semanticscholar.org/paper/NerveNet:-Learning-Structured-Policy-with-Graph-Wang-Liao/249408527106d7595d45dd761dd53c83e5a02613).
-- 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.
+- 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](https://doi.org/10.48550/arXiv.2007.04976).

docs/design/learning_algorithm.md

@@ -25,6 +25,6 @@ Alternative learning algorithms include:
 
 **References**
 
-- 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.
+- 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](https://proceedings.mlr.press/v80/fujimoto18a.html).
-- 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.
+- 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](https://doi.org/10.48550/arXiv.2007.04976).
-- 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.
+- 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](https://doi.org/10.48550/arXiv.1707.06347).

docs/javascripts/mathjax.js

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mkdocs.yml

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