About Our Group
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.
- 05/2020: Another work related to POMDP tasks [On Improving the Learning of Long-Term historical Information for Tasks with Partial Observability] is published.
- 05/2020: A RL paper is submitted to NeurIPS 2020.
- 04/2020: Collaborating with Ubisoft, we organized some competitions to test our RL agent in Rabbids: Journey To The West, and our RL agent won all the games.
- 02/2020: Zhang Li gives the oral presentation of our UVIN paper in a recorded video at AAAI2020 due to the COVID-19 pandemic.
- 12/2019: The students won the best technical award in the Data Hackathon competition hosted by Ubisoft.
- 12/2019: Congratulations to Zhang Li. Our UVIN paper got accepted for AAAI 2020 oral presenations.
- 04/2017: The group submit our ADRQN paper named On Improving Deep Reinforcement Learning for POMDPs to Arxiv (cited 30 times).