151 citations · 188 across the 6 of their papers we have counts for
6 papers
LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
Yizhou Zhao, Kaixiang Lin, Zhiwei Jia +4
Learning-based methods for training embodied agents typically require a large number of high-quality scenes that contain realistic layouts and support meaningful interactions. Howe…
Off-Policy Imitation Learning from Observations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai +1
Learning from Observations (LfO) is a practical reinforcement learning scenario from which many applications can benefit through the reuse of incomplete resources. Compared to conv…
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control
Dong Chen, Kaian Chen. Zhaojian Li, Tianshu Chu +3
This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-base…
Transfer Learning in Deep Reinforcement Learning: A Survey
Zhuangdi Zhu, Kaixiang Lin, Anil K. Jain +1
Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fa…
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai +1
Model-free deep reinforcement learning (RL) has demonstrated its superiority on many complex sequential decision-making problems. However, heavy dependence on dense rewards and hig…
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet Management
Kaixiang Lin, Renyu Zhao, Zhe Xu +1
Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportati…