From 97598105e5d5e7e70cdd709ca5a7363d0301b7eb Mon Sep 17 00:00:00 2001 From: Tibo De Peuter Date: Tue, 19 May 2026 23:11:20 +0200 Subject: [PATCH] docs: latex and urls --- docs/HPC.md | 2 +- docs/README.md | 4 ++++ docs/api/tracking.md | 2 +- docs/design/actor-critic.md | 8 ++++---- docs/design/communication.md | 4 ++-- docs/design/learning_algorithm.md | 6 +++--- docs/javascripts/mathjax.js | 18 ++++++++++++++++++ mkdocs.yml | 6 ++++++ 8 files changed, 39 insertions(+), 11 deletions(-) create mode 100644 docs/javascripts/mathjax.js diff --git a/docs/HPC.md b/docs/HPC.md index c4ebf6a..e7a3372 100644 --- a/docs/HPC.md +++ b/docs/HPC.md @@ -1,6 +1,6 @@ # HPC Guide -Full documentation: +Full documentation: [https://docs.hpc.ugent.be/](https://docs.hpc.ugent.be/) ## Storage Overview diff --git a/docs/README.md b/docs/README.md index 237a1a5..df66859 100644 --- a/docs/README.md +++ b/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?" diff --git a/docs/api/tracking.md b/docs/api/tracking.md index 226992b..00eed22 100644 --- a/docs/api/tracking.md +++ b/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 diff --git a/docs/design/actor-critic.md b/docs/design/actor-critic.md index 245fefd..d27dbc3 100644 --- a/docs/design/actor-critic.md +++ b/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/ -- 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. -- 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. +- 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](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](https://www.semanticscholar.org/paper/NerveNet:-Learning-Structured-Policy-with-Graph-Wang-Liao/249408527106d7595d45dd761dd53c83e5a02613). diff --git a/docs/design/communication.md b/docs/design/communication.md index 73b1220..377de89 100644 --- a/docs/design/communication.md +++ b/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. -- 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. +- 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](https://doi.org/10.48550/arXiv.2007.04976). diff --git a/docs/design/learning_algorithm.md b/docs/design/learning_algorithm.md index 1b5c2f5..afee110 100644 --- a/docs/design/learning_algorithm.md +++ b/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. -- 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. -- 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. +- 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](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](https://doi.org/10.48550/arXiv.1707.06347). diff --git a/docs/javascripts/mathjax.js b/docs/javascripts/mathjax.js new file mode 100644 index 0000000..f5e96e7 --- /dev/null +++ b/docs/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(); +}); diff --git a/mkdocs.yml b/mkdocs.yml index 6c46844..3084148 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -8,3 +8,9 @@ markdown_extensions: - name: mermaid class: mermaid format: !!python/name:pymdownx.superfences.fence_code_format + - pymdownx.arithmatex: + generic: true + +extra_javascript: + - javascripts/mathjax.js + - https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js