Data-Driven Decision-Making Algorithms

Wed, 04 Oct 2023 00:00:00 +0000

A main research direction for the AIR-DREAM Lab is to develop high-performance, robust, generalizable, and real-world deployable data-driven decision-making algorithms. We are specifically interested in offline policy learning methods, such as offline reinforcement learning (RL), offline imitation learning (IL), and offline planning, which enable a simulation-free and low-cost solution to address many real-world problems.

Our current research focus includes:

Sample-efficient / high-generalization offline RL / IL / planning algorithms

Algorithms | MODE Collaboration

Data-Driven Decision-Making Algorithms

Our current research focus includes:

Sample-efficient / high-generalization offline RL / IL / planning algorithms

Foundation models for decision-making

Safe offline RL algorithms

Hybrid RL that combines offline and online policy learning

Offline policy learning under imperfect reward

Feedback-efficient RLHF