Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach

TL;DR

This paper introduces DIPPER, a hierarchical reinforcement learning framework that uses direct preference optimization to address non-stationarity and infeasible subgoals, leading to significant performance improvements.

Contribution

DIPPER formulates goal-conditioned HRL as a bi-level optimization problem and employs preference-based training to enhance stability and feasibility in hierarchical policies.

Findings

DIPPER achieves up to 40% performance improvements over baselines.

The framework effectively mitigates non-stationarity in hierarchical RL.

It reduces infeasible subgoal generation in complex tasks.

Abstract

Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However, HRL methods face two fundamental challenges: (i) non-stationarity caused by the evolving lower-level policy during training, which destabilizes higher-level learning, and (ii) the generation of infeasible subgoals that lower-level policies cannot achieve. To address these challenges, we introduce DIPPER, a novel HRL framework that formulates goal-conditioned HRL as a bi-level optimization problem and leverages direct preference optimization (DPO) to train the higher-level policy. By learning from stationary preference comparisons over subgoal sequences rather than rewards that depend on the evolving lower-level policy, DIPPER mitigates the impact of non-stationarity on hierarchical learning. To address infeasible subgoals, DIPPER incorporates lower-level value function regularization that encourages the higher-level policy to propose achievable subgoals. We also introduce two novel metrics to quantitatively verify that DIPPER mitigates non-stationarity and infeasible subgoal generation issues in HRL. We perform empirical evaluations on challenging robotic navigation and manipulation benchmarks and show that DIPPER achieves upto 40% improvements over state-of-the-art baselines, demonstrating that preference-based methods can effectively alleviate persistent challenges in hierarchical