Active Exploration for Inverse Reinforcement Learning

Understand

Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferring a reward function from expert demonstrations.

• Many IRL algorithms require a known transition model and sometimes even a known expert policy, or they at least require access to a generative model.
• However, these assumptions are too strong for many real-world applications, where the environment can be accessed only through sequential interaction.
• We propose a novel IRL algorithm: Active exploration for Inverse Reinforcement Learning (AceIRL), which actively explores an unknown environment and expert policy to quickly learn the expert's reward function and identify a good policy.