Q-based Variational Inverse Reinforcement Learning (QVIRL): Scalable Bayesian IRL from Raw Pixels

Forum topic · 小凯 · 2026-08-19

Summary

This forum post introduces QVIRL, a novel Bayesian inverse reinforcement learning (IRL) method presented in arXiv paper 2608.16888 by Ondrej Bajgar, Peter Tisnikar, Alessandro Abate and colleagues. QVIRL addresses the challenge of inferring human preferences, represented as reward functions, from expert demonstrations when manual specification of preferences is infeasible. The method works by primarily learning a variational distribution over optimal Q-values, from which it recovers a posterior distribution over rewards. Its distinguishing feature is combining scalability with uncertainty quantification—capabilities important for safety-critical applications and active learning. The authors demonstrate strong apprenticeship learning performance across gridworlds, Lunar Lander, highway environments, and two ATARI games, using both static expert datasets and active learning. Notably, QVIRL is the first Bayesian IRL method shown to train from raw pixel observations. The post includes both a Chinese summary and the original English abstract.

Paper Overview

Field: Machine Learning
Authors: Ondrej Bajgar, Peter Tisnikar, Alessandro Abate et al. (5 authors)
Published: 2026-08-17
arXiv: 2608.16888

Summary (translated from the Chinese post)

Safe and beneficial AI requires systems that can learn and act according to human preferences, but explicitly specifying these preferences by hand is often infeasible. Inverse reinforcement learning (IRL) addresses this challenge by inferring preferences—represented as reward functions—from expert behaviour.

The authors introduce Q-based Variational IRL (QVIRL), a novel Bayesian IRL method that recovers a posterior distribution over rewards from expert demonstrations by primarily learning a variational distribution over optimal Q-values. Unlike previous approaches, QVIRL combines scalability with uncertainty quantification, which is important both for safety-critical applications and for active learning.

Key results:

Strong apprenticeship learning performance across a variety of tasks: gridworlds, Lunar Lander, highway environments, and two ATARI games.
Validated with both static expert datasets and active learning settings.
To the authors' knowledge, this is the first Bayesian IRL method demonstrated to train from raw pixel observations.

Original Abstract (excerpt)

> The development of safe and beneficial AI requires that systems can learn and act in accordance with human preferences. However, explicitly specifying these preferences by hand is often infeasible. Inverse reinforcement learning (IRL) addresses this challenge by inferring preferences, represented as reward functions, from expert behaviour. We introduce Q-based Variational IRL (QVIRL), a novel Bayesian IRL method that recovers a posterior distribution over rewards from expert demonstrations via primarily learning a variational distribution over optimal Q-values. Unlike previous approaches, QVIRL combines scalability with uncertainty quantification, important for safety-critical applications as well as active learning. We demonstrate QVIRL's strong performance in apprenticeship learning acro...

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