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) aims to facilitate a learner's ability to imitate expert
behavior by acquiring reward functions that explain the expert's decisions. Regularized IRL …

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 …

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 attempts to reconstruct the reward function in a Markov
decision problem, using observations of agent actions. As already observed in Russell …

Recent methods for imitation learning directly learn a $ Q $-function using an implicit reward
formulation rather than an explicit reward function. However, these methods generally …

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 …

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 …

How well do reward functions learned with inverse reinforcement learning (IRL) generalize?
We illustrate that state-of-the-art IRL algorithms, which maximize a maximum-entropy …

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 …