Reinforcement Learning | KAIST MatriX

Tag: Reinforcement Learning

Research Highlight

Reinforcement Learning Algorithm for Batch Chemical Process Control

Haeun Yoo, a member of Prof. Jay H. Lee’s group, has proposed a phase segmentation approach and modified deep deterministic policy gradient algorithm for batch process optimal control under uncertainty....read more

Actor-Critic AI Applications Batch process KAIST Institute for Artificial Intelligence Machine Learning Optimal control Reinforcement Learning

Research Highlight

Self-Supervised Learning to Distill Hierarchy in High-Dimensional Dynamic Systems

Prof. Han-Lim Choi’s research team has developed a learning framework to control a high-dimensional robotic system that distills underlying hierarchical structure in robot motion data. By learning high-level intentions as well as low-level control actions, the proposed framework enables adaptation of policy learned from a certain task to much more diverse sets of tasks. ...read more

AI for Cooperative Robots Deep Learning High-Dimensional Systems Interpretable AI KI for Robotics Reinforcement Learning Representation Learning Robust Intelligence Under Uncertainty

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