Group Robust Preference Optimization in Reward-free RLHF

2 code implementations • 30 May 2024 • Shyam Sundhar Ramesh, Yifan Hu, Iason Chaimalas, Viraj Mehta, Pier Giuseppe Sessa, Haitham Bou Ammar, Ilija Bogunovic

Our approach builds upon reward-free direct preference optimization methods, but unlike previous approaches, it seeks a robust policy which maximizes the worst-case group performance.

Distributionally Robust Model-based Reinforcement Learning with Large State Spaces

no code implementations • 5 Sep 2023 • Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic

Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment.

Gaussian Processes Model-based Reinforcement Learning +1

Movement Penalized Bayesian Optimization with Application to Wind Energy Systems

no code implementations • 14 Oct 2022 • Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Andreas Krause, Ilija Bogunovic

Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-making given side information, with important applications, e. g., in wind energy systems.

Bayesian Optimization Decision Making