tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
Archived
1
Fork 0
Code Issues Releases Activity

Deployed 9759810 with MkDocs version: 1.6.1

Browse source
This commit is contained in:
github-actions[bot] 2026-05-19 21:11:40 +00:00
parent 281bae4df2
commit 1d2a68dc54
21 changed files with 128 additions and 32 deletions
Download patch file Download diff file Expand all files Collapse all files

10
design/learning_algorithm/index.html
View file

@ -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>
<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>