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 …

It is often difficult to hand-specify what the correct reward function is for a task, so
researchers have instead aimed to learn reward functions from human behavior or …

Interactive reinforcement learning (RL) has been successfully used in various applications in
different fields, which has also motivated HCI researchers to contribute in this area. In this …

Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the
inverse reinforcement learning problem. Unfortunately current methods generally do not …

We seek to align agent behavior with a user's objectives in a reinforcement learning setting
with unknown dynamics, an unknown reward function, and unknown unsafe states. The user …

The standard formulation of Reinforcement Learning lacks a practical way of specifying what
are admissible and forbidden behaviors. Most often, practitioners go about the task of …

Alignment of human and autonomous agent values and objectives is vital in human-
autonomy teaming settings which require collaborative action toward a common goal. In …

Driven by the algorithmic advancements in reinforcement learning and the increasing
number of implementations of human-AI collaboration, Collaborative Reinforcement …

Reward learning algorithms utilize human feedback to infer a reward function, which is then
used to train an AI system. This human feedback is often a preference comparison, in which …

As robots become increasingly prevalent in our communities, aligning the values motivating
their behavior with human values is critical. However, it is often difficult or impossible for …