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docs/design/actor-critic.md
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## 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).

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docs/design/communication.md
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@ -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).

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docs/design/learning_algorithm.md
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**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).