CSI-Agent group works on incorporation of data-driven multi-task, multi-modal, continuous learning model architectures in the context of reinforcement learning to address the problems (sample-inefficiency, low generalization) of real-world adoption of reinforcement learning. CSI-Agent group develops pretrained behavior-driven models that can be adopted for downstream agent task learning in real-world settings.
CSI-Agent group conducts the research on various embodied agent scenarios including drone, robot, and autonomous driving where the embodied agent learns to take optimal actions for achieving its goals in the environment, and can adapt its behaviors to changes in the environment or its goals. To do so, CSI-Agent group employs several machine learning techniques such as imitation learning, reinforcement learning, and multi-modal learning.
CSI-Agent group focuses on system intelligence areas including network-storage-learning system optimization, autonomous system control, federated learning with NPUs, and networked real-time CPS (Cyber-Physical System), aiming at the application of data-driven deep learning technologies on large-scale real-world system automation that requires optimal sequential decisions.
Please check more information about us at our homepage!