Diffusion model improves long-range goal planning in offline reinforcement learning
Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
Artificial Intelligence
Summary
Goal-conditioned reinforcement learning helps robots or agents reach specific goals using past experience without new feedback. However, in long tasks, usual methods struggle because the value estimates are unstable when rewards are rare and distant. The authors offer a solution called Diffusion Subgoal Planning (DSP), which uses a generative model to create intermediate steps (subgoals) toward the final goal, removing reliance on unstable value signals. Their approach helps agents plan better in complicated environments, like mazes, by producing reachable and goal-focused subgoals.
What this means in practice
- •For robotics engineers: Plan complex multi-step navigation tasks by generating intermediate waypoints without relying on unstable value estimates.
- •For automation system developers: Enhance goal-directed control in manipulation tasks using learned subgoal generation to improve reliability in uncertain environments.
Authors
Hengrui Zhang, Yuhu Cheng, C. L. Philip Chen, Xuesong Wang
Abstract
Offline goal-conditioned reinforcement learning (GCRL) learns goal-directed policies from reward-free data, but in long-horizon tasks, goal-conditioned value functions often provide unstable guidance due to sparse rewards and discounting. Hierarchical methods partially mitigate this issue via subgoal decomposition; however, high-level decision-making still relies on noise-sensitive value estimates, leading to unstable behavior in complex environments. We address this limitation by proposing \textbf{D}iffusion \textbf{S}ubgoal \textbf{P}lanning (\textbf{DSP}), a diffusion-based framework for high-level subgoal generation. DSP casts high-level planning as guided generative inference over goal-conditioned subgoals and learns both conditional and unconditional flows, enabling classifier-free guidance to introduce a goal-directed bias at inference time. By removing explicit value-based guidance from high-level planning, DSP generates reachable and goal-directed subgoals through a generative model while retaining hierarchical execution. Experiments on offline GCRL benchmarks demonstrate that DSP outperforms prior methods on a range of navigation and manipulation tasks, with particularly strong performance in maze environments that require multi-step subgoal planning.