Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a
suitably chosen reward function. However, designing such a reward function often requires …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

In preference-based reinforcement learning (RL), an agent interacts with the environment
while receiving preferences instead of absolute feedback. While there is increasing research …

Preference-based reinforcement learning (PbRL) develops agents using human
preferences. Due to its empirical success, it has prospect of benefiting human-centered …

Preference-based reinforcement learning has gained significant popularity over the years,
but it is still unclear what exactly preference learning is and how it relates to other …

The recent surge of versatile large language models (LLMs) largely depends on aligning
increasingly capable foundation models with human intentions by preference learning …

The goal of task transfer in reinforcement learning is migrating the action policy of an agent
to the target task from the source task. Given their successes on robotic action planning …

Reinforcement learning algorithms are usually hard to use for non expert users. It is required
to consider several aspects like the definition of state-, action-and reward-space as well as …

In this work, I propose to bridge the gap between human users and adaptive control of
robotic systems. The goal is to enable robots to consider user feedback and adjust their …

As intelligent agents become pervasive in our lives, they are expected to not only achieve
tasks alone but also engage in tasks with humans in the loop. In such cases, the human …