Uncertainty-Regularized Diffusional Subgoals for Hierarchical Reinforcement Learning
Keywords: Hierarchical Reinforcement Learning
TL;DR: We propose a subgoal generation method for HRL using conditional diffusion models and a Gaussian Process (GP) prior to address non-stationarity in off-policy training, improving subgoal regularization and exploration in uncertain areas.
Abstract: Hierarchical reinforcement learning (HRL) aims to solve complex tasks by making decisions across multiple levels of temporal abstraction. However, off-policy training of hierarchical policies faces non-stationarity issues because the low-level policy is constantly changing, which makes it difficult for the high-level policy that generates subgoals to adapt. In this paper, we propose a conditional diffusion model-based approach for subgoal generation to mitigate these non-stationarity challenges. Specifically, we employ a Gaussian Process (GP) prior on subgoal generation as a surrogate distribution to regularize the diffusion policy and inform the diffusion process about uncertain areas in the action space. We introduce adaptive inducing states to facilitate sparse GP-based subgoal generation, enhancing sample efficiency and promoting better exploration in critical regions of the state space. Building on this framework, we develop an exploration strategy that identifies promising subgoals based on the learned predictive distribution of the diffusional subgoals. Experimental results demonstrate significant improvements in both sample efficiency and performance on challenging continuous control benchmarks compared to prior HRL methods.
Primary Area: reinforcement learning
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Submission Number: 11671
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