4 papers
PAC Apprenticeship Learning with Bayesian Active Inverse Reinforcement Learning
Ondrej Bajgar, Dewi S. W. Gould, Jonathon Liu +3
As AI systems become increasingly autonomous, reliably aligning their decision-making with human preferences is essential. Inverse reinforcement learning (IRL) offers a promising a…
Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)
Keyan Miao, Liqun Zhao, Han Wang +2
Designing controllers that achieve task objectives while ensuring safety is a key challenge in control systems. This work introduces Opt-ODENet, a Neural ODE framework with a diffe…
Walking the Values in Bayesian Inverse Reinforcement Learning
Ondrej Bajgar, Alessandro Abate, Konstantinos Gatsis +1
The goal of Bayesian inverse reinforcement learning (IRL) is recovering a posterior distribution over reward functions using a set of demonstrations from an expert optimizing for a…
Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations
Liqun Zhao, Keyan Miao, Konstantinos Gatsis +1
Reinforcement learning (RL) excels in applications such as video games, but ensuring safety as well as the ability to achieve the specified goals remains challenging when using RL…