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 …

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 …

Abstract Inverse Reinforcement Learning (IRL) describes the problem of learning an
unknown reward function of a Markov Decision Process (MDP) from observed behavior of …

Two main challenges in Reinforcement Learning (RL) are designing appropriate reward
functions and ensuring the safety of the learned policy. To address these challenges, we …

Inverse reinforcement learning (IRL) methods assume that the expert data is generated by
an agent optimizing some reward function. However, in many settings, the agent may …

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 The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function R from
a policy pi. To do this, we need a model of how pi relates to R. In the current literature, the …

Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …

Inverse reinforcement learning (IRL) aims to recover the reward function and the associated
optimal policy that best fits observed sequences of states and actions implemented by an …

Inverse reinforcement learning (IRL) is the problem of recovering the underlying reward
function from the behavior of an expert. Most of the existing IRL algorithms assume that the …