We consider a setting for Inverse Reinforcement Learning (IRL) where the learner is
extended with the ability to actively select multiple environments, observing an agent's …

Learning desirable behavior from a limited number of demonstrations, also known as
inverse reinforcement learning, is a challenging task in machine learning. I apply maximum …

Inverse reinforcement learning (IRL) has seen significant advancements in recent years.
This class of approaches aims to efficiently learn the underlying reward function that …

While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on
estimating a reward function that best explains an expert agent's policy or demonstrated …

Inverse Reinforcement Learning (IRL)---the problem of learning reward functions from
demonstrations of an\emph {expert policy}---plays a critical role in developing intelligent …

Inverse Reinforcement Learning (IRL)---the problem of learning reward functions from
demonstrations of an\emph {expert policy}---plays a critical role in developing intelligent …

Learning a reward function from demonstrations suffers from low sample-efficiency. Even
with abundant data, current inverse reinforcement learning methods that focus on learning …

Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a
reward function justifying the behavior demonstrated by an expert agent. A well-known …

Abstract Inverse Reinforcement Learning addresses the problem of inferring an expert's
reward function from demonstrations. However, in many applications, we not only have …

Inverse reinforcement learning (IRL) aims to recover the reward function of an expert agent
from demonstrations of behavior. It is well known that the IRL problem is fundamentally ill …