Understanding decision-making is a core goal in both neuroscience and psychology, and
computational models have often been helpful in the pursuit of this goal. While many models …

The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including
value alignment and robot learning from demonstration. Despite significant algorithmic …

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 …

Reward learning enables robots to learn adaptable behaviors from human input. Traditional
methods model the reward as a linear function of hand-crafted features, but that requires …

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 …

Reinforcement learning demands a reward function, which is often difficult to provide or
design in real world applications. While inverse reinforcement learning (IRL) holds promise …

We consider a Bayesian approach to offline model-based inverse reinforcement learning
(IRL). The proposed framework differs from existing offline model-based IRL approaches by …

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 …

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 …

Designing intelligent and robust autonomous navigation systems remains a great challenge
in mobile robotics. Inverse reinforcement learning (IRL) offers an efficient learning technique …