Many imitation learning (IL) algorithms employ inverse reinforcement learning (IRL) to infer
the intrinsic reward function that an expert is implicitly optimizing for based on their …

Imitation learning in a high-dimensional environment is challenging. Most inverse
reinforcement learning (IRL) methods fail to outperform the demonstrator in such a high …

In Imitation Learning (IL), utilizing suboptimal and heterogeneous demonstrations presents a
substantial challenge due to the varied nature of real-world data. However, standard IL …

Imitation Learning (IL) is an effective learning paradigm exploiting the interactions between
agents and environments. It does not require explicit reward signals and instead tries to …

Adversarial Imitation Learning (AIL) is a broad family of imitation learning methods designed
to mimic expert behaviors from demonstrations. While AIL has shown state-of-the-art …

Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert
demonstrations to facilitate policy learning, and has demonstrated its remarkable success in …

A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to
significantly outperform the demonstrator. This is because IRL typically seeks a reward …

Imitation learning (IL) is a framework that learns to imitate expert behavior from
demonstrations. Recently, IL shows promising results on high dimensional and control tasks …

Often times in imitation learning (IL), the environment we collect expert demonstrations in
and the environment we want to deploy our learned policy in aren't exactly the same (eg …

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 …