Hi ! We are a team of
Deep Reinforcement Learning .
Welcome to join our team !
Following the stunning success of AlphaGo, Deep Reinforcement Learning (DRL) combining deep learning and conventional reinforcement learning has emerged as one of the most competitive approaches for learning in sequential decision making problems. It links with the advanced machine learning theory, optimization theory and statistical theory, which sets a complex task for the students who are interested in this field.
Driven/inspired by many talented researchers, our 1029DRL Group (led by [礼欣老师] [A/Prof. Xin Li] keeps investigating the emerging DRL algorithms published in the top conferences, aiming to develop our own to contribute to the RL community. We devote to developing a more explainable/sample-efficient/training-efficient/robust (D)RL algorithm with applications to single/multi-player games, robotics and healthcare.
We are currently recruiting Ph.D candidates, master students, senior undergradate students to join our group to work with us and share the thoughts.
咳咳，下面是一段生动的中文介绍：
AlphaGo 的横空出世让深度强化学习 (DRL) 站在了聚光灯下，它像是一个只有最聪明的大脑才能解开的谜题。我们 1029 实验室就是在这一领域乘风破浪的冲浪手。虽然 DRL 结合了深度学习和强化学习，听起来有点高深莫测，但我们从未停止探索的脚步。
在[礼欣老师]的带领下，我们跟踪顶会前沿，致力于创造属于我们自己的、更具有可解释性的、参数&训练高效、鲁棒的强化学习算法，让算法不再是“黑盒”，让智能体学得更快、表现更稳，能在游戏、机器人和医疗领域大显身手。
如果你也对深度强化学习充满兴趣，无论你是博士、硕士还是本科大神，欢迎加入我们，一起在这个充满挑战也充满乐趣的领域里快乐地探索和研究！
- Ming Wang [Offline Meta-Reinforcement Learning with Flow-Based Task Inference and Adaptive Correction of Feature Overgeneralization] (Oral Presentation)
- Ruobing Wang [Generative Branching for Mixed-Integer Linear Programming]
- Meiju Li and Ruixiang Sun [From Tokens to Latent States: Leveraging Pre-trained Language Models for Improving Partially Observable Reinforcement Learning]