Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

TL;DR

This paper introduces a novel penalty-based framework for solving complex bilevel reinforcement learning problems, including RLHF, with theoretical analysis and demonstrated effectiveness in simulations.

Contribution

It presents the first principled penalty formulation approach for bilevel RL problems, extending beyond static objectives to dynamic, real-world scenarios.

Findings

Effective algorithms demonstrated in simulations

Theoretical analysis of problem landscape

Successful application to RLHF and incentive design

Abstract

Bilevel optimization has been recently applied to many machine learning tasks. However, their applications have been restricted to the supervised learning setting, where static objective functions with benign structures are considered. But bilevel problems such as incentive design, inverse reinforcement learning (RL), and RL from human feedback (RLHF) are often modeled as dynamic objective functions that go beyond the simple static objective structures, which pose significant challenges of using existing bilevel solutions. To tackle this new class of bilevel problems, we introduce the first principled algorithmic framework for solving bilevel RL problems through the lens of penalty formulation. We provide theoretical studies of the problem landscape and its penalty-based (policy) gradient algorithms. We demonstrate the effectiveness of our algorithms via simulations in the Stackelberg Markov game, RL from human feedback and incentive design.