Towards Efficient Personalized Driver Behavior Modeling with Machine Unlearning

Driver Behavior Modeling (DBM) aims to predict and model human driving behaviors, which is typically incorporated into the Advanced Driver Assistance System to enhance transportation safety and improve driving experience. Inverse reinforcement learning (IRL) is a prevailing DBM technique with the goal of modeling the driving policy by...

Training for Implicit Norms in Deep Reinforcement Learning Agents through Adversarial Multi-Objective Reward Optimization

We propose a deep reinforcement learning algorithm that employs an adversarial training strategy for adhering to implicit human norms alongside optimizing for a narrow goal objective. Previous methods which incorporate human values into reinforcement learning algorithms either scale poorly or assume hand-crafted state features. Our algorithm...

Preference-driven demonstrations ranking for inverse reinforcement learning

New flexible teaching methods for robotics are needed to automate repetitive tasks that are currently still done by humans. For limited batch sizes, it is too expensive to teach a robot a new task (Smith & Anderson, 2014). Ideally, such flexible robots can be taught a new task by a non-expert. A non-expert is a person who knows the task the...