Update cleanrl-supported-papers-projects.md

docs/cleanrl-supported-papers-projects.md

@@ -6,6 +6,8 @@ CleanRL has become an increasingly popular deep reinforcement learning library,
 
 ## Publications
 
+* 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)
+
 * 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)
 
 * 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)
@@ -27,4 +29,4 @@ CleanRL has become an increasingly popular deep reinforcement learning library,
 
 * 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)
 
-* 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)
+* 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)