We present COIL (Counterfactual Object Interaction Learning), a novel way of learning skills
of object interactions on entity-centric environments. The goal is to learn primitive behaviors …

While reinforcement learning (RL) algorithms have been successfully applied to numerous
tasks, their reliance on neural networks makes their behavior difficult to understand and …

To effectively track resident space objects (RSOs), the tasking of sensors in a distributed
network requires flexible and adaptive approaches. While Deep Reinforcement Learning …

This research paper delves into the application of Deep Reinforcement Learning (DRL) in
asset-class agnostic portfolio optimization, integrating industry-grade methodologies with …

This paper introduces Personalized Path Recourse, a novel method that generates recourse
paths for an agent. The objective is to achieve desired goals (eg, better outcomes compared …

We consider the effectiveness of multi-objective counterfactual explanations (MOCE) in
helping individuals learn tactics, or rules of thumb, to apply when required to select a course …

Reinforcement learning (RL) algorithms often use neural networks to represent agent's
policy, making them difficult to interpret. Counterfactual explanations are human-friendly …

Abstract Machine learning is progressing at an astounding rate. The past decade has seen
an explosion in the amount of machine learning research, including deep learning …

As progress in AI impacts all sectors of society, the world is destined to see increasingly
complex and numerous autonomous decision-making agents, which act upon their …