From b746bb4299f17b2e4cf9eaf0c1ff2c1c2520ac7e Mon Sep 17 00:00:00 2001 From: Md Masudur Rahman Date: Tue, 8 Nov 2022 14:10:19 -0500 Subject: [PATCH] Update cleanrl-supported-papers-projects.md Adding a paper that usages CleanRL for implementation. --- docs/cleanrl-supported-papers-projects.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/cleanrl-supported-papers-projects.md b/docs/cleanrl-supported-papers-projects.md index 0598c7734..390983745 100644 --- a/docs/cleanrl-supported-papers-projects.md +++ b/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) \ No newline at end of file +* 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)