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 …

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 …

We consider the problem of imitation learning where the examples, demonstrated by an
expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) …

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 …

This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL),
namely the reward extrapolation error, where the learned reward function may fail to explain …

Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering
can be tedious. However, prior IRL algorithms use on-policy transitions, which require …

The inverse reinforcement learning approach to imitation learning is a double-edged sword.
On the one hand, it can enable learning from a smaller number of expert demonstrations …

Abstract Inverse Reinforcement Learning (IRL) is an effective approach to recover a reward
function that explains the behavior of an expert by observing a set of demonstrations. This …

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 …

Abstract Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferring a reward
function from expert demonstrations. Many IRL algorithms require a known transition model …