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Update cleanrl-supported-papers-projects.md (#316)
Adding a paper that usages CleanRL for implementation.
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docs/cleanrl-supported-papers-projects.md

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## Publications
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* Md Masudur Rahman and Yexiang Xue. "Bootstrap Advantage Estimation for Policy Optimization in Reinforcement Learning." In Proceedings of the IEEE International Conference on Machine Learning and Applications (ICMLA), 2022. [https://arxiv.org/pdf/2210.07312.pdf](https://arxiv.org/pdf/2210.07312.pdf)
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* Centa, Matheus, and Philippe Preux. "Soft Action Priors: Towards Robust Policy Transfer." arXiv preprint arXiv:2209.09882 (2022). [https://arxiv.org/pdf/2209.09882.pdf](https://arxiv.org/pdf/2209.09882.pdf)
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* Weng, Jiayi, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu et al. "Envpool: A highly parallel reinforcement learning environment execution engine." In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. [https://openreview.net/forum?id=BubxnHpuMbG](https://openreview.net/forum?id=BubxnHpuMbG)
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* Huang, Shengyi, and Santiago Ontañón. "Action guidance: Getting the best of sparse rewards and shaped rewards for real-time strategy games." AIIDE Workshop on Artificial Intelligence for Strategy Games, [https://arxiv.org/abs/2010.03956](https://arxiv.org/abs/2010.03956)
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* Huang, Shengyi, and Santiago Ontañón. "Comparing Observation and Action Representations for Deep Reinforcement Learning in $\mu $ RTS." AIIDE Workshop on Artificial Intelligence for Strategy Gamee, October 2019 [https://arxiv.org/abs/1910.12134](https://arxiv.org/abs/1910.12134)
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* Huang, Shengyi, and Santiago Ontañón. "Comparing Observation and Action Representations for Deep Reinforcement Learning in $\mu $ RTS." AIIDE Workshop on Artificial Intelligence for Strategy Gamee, October 2019 [https://arxiv.org/abs/1910.12134](https://arxiv.org/abs/1910.12134)

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