MAP Inference for Bayesian Inverse Reinforcement Learning


The difficulty in inverse reinforcement learning (IRL) arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behaviour data as optimal. Using a Bayesian framework, we address this challenge by using the maximum a posteriori (MAP) estimation for the reward function, and show that… (More)


4 Figures and Tables


Citations per Year

Citation Velocity: 23

Averaging 23 citations per year over the last 3 years.

Learn more about how we calculate this metric in our FAQ.

Slides referencing similar topics