A Divergence Minimization Perspective on Imitation Learning Methods (1911.02256v1)

Abstract: In many settings, it is desirable to learn decision-making and control policies through learning or bootstrapping from expert demonstrations. The most common approaches under this Imitation Learning (IL) framework are Behavioural Cloning (BC), and Inverse Reinforcement Learning (IRL). Recent methods for IRL have demonstrated the capacity to learn effective policies with access to a very limited set of demonstrations, a scenario in which BC methods often fail. Unfortunately, due to multiple factors of variation, directly comparing these methods does not provide adequate intuition for understanding this difference in performance. In this work, we present a unified probabilistic perspective on IL algorithms based on divergence minimization. We present $f$-MAX, an $f$-divergence generalization of AIRL [Fu et al., 2018], a state-of-the-art IRL method. $f$-MAX enables us to relate prior IRL methods such as GAIL [Ho & Ermon, 2016] and AIRL [Fu et al., 2018], and understand their algorithmic properties. Through the lens of divergence minimization we tease apart the differences between BC and successful IRL approaches, and empirically evaluate these nuances on simulated high-dimensional continuous control domains. Our findings conclusively identify that IRL's state-marginal matching objective contributes most to its superior performance. Lastly, we apply our new understanding of IL methods to the problem of state-marginal matching, where we demonstrate that in simulated arm pushing environments we can teach agents a diverse range of behaviours using simply hand-specified state distributions and no reward functions or expert demonstrations. For datasets and reproducing results please refer to https://github.com/KamyarGh/rl_swiss/blob/master/reproducing/fmax_paper.md .

• The paper introduces a unified probabilistic framework that bridges Behavioral Cloning and Inverse Reinforcement Learning through divergence minimization.
• It presents the f-MAX algorithm that generalizes AIRL by leveraging reverse KL divergence to achieve mode-seeking policy updates for robust performance.
• Empirical results in high-dimensional control tasks demonstrate that IRL methods outperform BC, particularly in sparse expert demonstration scenarios.

Insights from "A Divergence Minimization Perspective on Imitation Learning Methods"

The paper "A Divergence Minimization Perspective on Imitation Learning Methods" presents a comprehensive examination of Imitation Learning (IL), focusing on Behavioral Cloning (BC) and Inverse Reinforcement Learning (IRL). The authors propose a unified probabilistic framework for understanding IL algorithms through the lens of divergence minimization, enlightening the core differences between BC and IRL methods and engaging in an empirical analysis to validate their findings.

The paper introduces $f$-MAX, a new algorithm that generalizes AIRL (Adversarial Inverse Reinforcement Learning) by applying the principles of $f$-divergence. This methodology bridges various IL approaches, providing fresh insights into the algorithmic nuances of past methods such as GAIL (Generative Adversarial Imitation Learning) and AIRL. The divergence minimization perspective highlights why IRL methods tend to outperform BC, especially in scenarios with sparse expert demonstrations—a common challenge in practical applications.

A primary insight uncovered through this work is that IRL's focus on matching state-marginal distributions within the observed trajectories is instrumental to its superior performance relative to BC. In essence, IRL approaches like AIRL and GAIL, which employ adversarial techniques to match expert state-action distributions, inherently minimize divergences that result in more generalizable and robust policies than those produced by BC, which primarily focuses on action cloning.

The authors further explore the implication of choosing different divergence metrics, such as the forward and reverse Kullback-Leibler (KL) divergence, on the efficacy of IL methods. An important takeaway is that the reverse KL divergence, utilized in AIRL, promotes a more mode-seeking policy update, aligning closely with the optimal reward-structured paths in complex control tasks. This attribute is particularly advantageous when expert trajectory samples are limited, highlighting a potentially critical area of research to optimize IL methodologies further.

Through empirical validation on high-dimensional, continuous control environments like Mujoco, the paper corroborates these insights, demonstrating that IRL methods utilizing divergence minimization, particularly those employing mode-seeking divergence, outperform classical BC methods, especially in data-constrained settings.

The paper extends these foundational ideas into novel applications by addressing problems in state-marginal matching without reliance on reward functions or expert demonstrations. This extension underscores the versatility of the divergence minimization framework. The application of the framework to design policies that achieve varied behaviors by simply specifying desired state distributions is a testament to the potential of divergence-based learning beyond traditional IL scenarios.

The findings in this work suggest significant theoretical and practical implications for the field of IL. Theoretically, it offers a unified framework that simplifies the analysis of IL algorithms. Practically, it underscores the importance of divergence optimization in designing robust decision-making systems capable of functioning effectively with minimal demonstration data. As the field moves forward, addressing challenges like sample efficiency and exploring the applicability of divergence-based methods to broader IL problems will prove vital.

Overall, the paper provides a rigorous, insightful perspective that advances our understanding of IL from a probabilistic viewpoint and paves the way for future innovations in AI policy learning methodologies. As researchers continue to refine these techniques, leveraging the insights from this work, the field can look forward to more comprehensive and efficient IL solutions.