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 …

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) 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 a powerful framework for learning complex
behaviors from expert demonstrations. However, it traditionally requires repeatedly solving a …

Inverse reinforcement learning (IRL) is computationally challenging, with common
approaches requiring the solution of multiple reinforcement learning (RL) sub-problems …

Inverse reinforcement learning (IRL) algorithms often rely on (forward) reinforcement
learning or planning over a given time horizon to compute an approximately optimal policy …

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

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 …