A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

TL;DR

This paper introduces a new online learning-based reduction for imitation learning and structured prediction, enabling the training of stationary deterministic policies with strong theoretical guarantees and improved empirical performance.

Contribution

It proposes a novel iterative algorithm that reduces imitation learning to no-regret online learning, providing better theoretical and practical results.

Findings

Outperforms previous methods on imitation learning tasks

Achieves good performance with stationary deterministic policies

Demonstrates effectiveness on a benchmark sequence labeling problem

Abstract

Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem.