# Interactive Teaching Algorithms for Inverse Reinforcement Learning

## TL;DR

This paper introduces interactive teaching algorithms for inverse reinforcement learning, enabling a teacher to adaptively provide demonstrations that significantly accelerate the learner’s acquisition of policies, with proven convergence and practical improvements.

## Contribution

It proposes a novel interactive teaching framework for IRL, including algorithms for both omniscient and blackbox settings, and demonstrates substantial speed-ups in learning through experiments.

## Key findings

- Teaching algorithms accelerate IRL learning process.
- Convergence guarantees are established in the omniscient setting.
- Experiments show significant improvement over uninformative teaching.

## Abstract

We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher. More formally, we tackle the following algorithmic question: How could a teacher provide an informative sequence of demonstrations to an IRL learner to speed up the learning process? We present an interactive teaching framework where a teacher adaptively chooses the next demonstration based on learner's current policy. In particular, we design teaching algorithms for two concrete settings: an omniscient setting where a teacher has full knowledge about the learner's dynamics and a blackbox setting where the teacher has minimal knowledge. Then, we study a sequential variant of the popular MCE-IRL learner and prove convergence guarantees of our teaching algorithm in the omniscient setting. Extensive experiments with a car driving simulator environment show that the learning progress can be speeded up drastically as compared to an uninformative teacher.