Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit
constraints followed by expert agents from their demonstration data. As an emerging …

The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function $ R $ from a
policy $\pi $. This problem is difficult, for several reasons. First of all, there are typically …

Inverse reinforcement Learning (IRL) has emerged as a powerful paradigm for extracting
expert skills from observed behavior, with applications ranging from autonomous systems to …

Inverse reinforcement learning (IRL) aims to infer a reward from expert demonstrations,
motivated by the idea that the reward, rather than the policy, is the most succinct and …

In reinforcement learning, specifying reward functions that capture the intended task can be
very challenging. Reward learning aims to address this issue by learning the reward …

Inverse reinforcement learning (IRL) seeks to learn the reward function from expert
trajectories, to understand the task for imitation or collaboration thereby removing the need …

This paper introduces a novel stochastic control framework to enhance the capabilities of
automated investment managers, or robo-advisors, by accurately inferring clients' …

The aim of inverse reinforcement learning (IRL) is to infer an agent's preferences from
observing their behaviour. Usually, preferences are modelled as a reward function, $ R …

In scenarios of inverse reinforcement learning (IRL) with a single expert, adversarial inverse
reinforcement learning (AIRL) serves as a foundational approach to providing …

Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to
recover a reward for which the expert is optimal. This work proposes a model-free algorithm …