The utilization of reinforcement learning (RL) within the field of education holds the potential
to bring about a significant shift in the way students approach and engage with learning and …

This survey article has grown out of the RL4ED workshop organized by the authors at the
Educational Data Mining (EDM) 2021 conference. We organized this workshop as part of a …

Reward learning enables robots to learn adaptable behaviors from human input. Traditional
methods model the reward as a linear function of hand-crafted features, but that requires …

Decisions are increasingly taken by both humans and machine learning models. However,
machine learning models are currently trained for full automation—they are not aware that …

We study the problem of inverse reinforcement learning (IRL) with the added twist that the
learner is assisted by a helpful teacher. More formally, we tackle the following algorithmic …

Most supervised learning models are trained for full automation. However, their predictions
are sometimes worse than those by human experts on some specific instances. Motivated by …

We study black-box reward poisoning attacks against reinforcement learning (RL), in which
an adversary aims to manipulate the rewards to mislead a sequence of RL agents with …

The financial sector presents many opportunities to apply various machine learning
techniques. Centralized machine learning creates a constraint which limits further …

When a person is not satisfied with how a robot performs a task, they can intervene to correct
it. Reward learning methods enable the robot to adapt its reward function online based on …

The emergence of the Internet-of-Things (IoT) sheds light on applying the machine teaching
(MT) algorithms for online personalized education on home devices. This direction becomes …