Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL

Researchers introduce Chain-of-Goals Hierarchical Policy (CoGHP), a novel framework that applies chain-of-thought reasoning to offline reinforcement learning by autoregressively generating sequences of intermediate subgoals to solve long-horizon tasks. The unified architecture demonstrates consistent performance improvements over existing hierarchical baselines on navigation and manipulation benchmarks.

CoGHP represents a meaningful advancement in offline reinforcement learning by addressing a fundamental challenge in long-horizon decision-making. Traditional hierarchical approaches rely on separate high- and low-level networks that generate single intermediate subgoals, constraining their ability to reason through complex multi-step problems. This research borrows cognitive principles from chain-of-thought prompting in large language models, applying similar reasoning patterns to robotic and navigation domains.

The technical innovation centers on reformulating hierarchical decision-making as autoregressive sequence modeling. Rather than treating subgoal generation as a separate process, CoGHP integrates state, goals, latent subgoals, and actions within a unified framework using an MLP-Mixer backbone architecture. This enables cross-token communication and captures structural relationships that single-subgoal approaches miss, effectively creating multiple "reasoning steps" before committing to primitive actions.

For the AI and robotics community, this work has practical implications for offline learning scenarios where online interaction is infeasible or costly. Deployment in manufacturing, autonomous systems, and robotic manipulation requires methods that function within fixed datasets, making offline RL increasingly relevant. The consistent improvements demonstrated across challenging benchmarks suggest the approach generalizes beyond toy problems.

The framework's effectiveness opens questions about scaling to even longer horizons and real-world deployment constraints. Future work might explore integration with vision transformers, application to multi-agent scenarios, or adaptation to partially observable environments. The project's public availability indicates the authors' commitment to reproducibility and community adoption.

• →CoGHP uses autoregressive sequence modeling to generate multiple intermediate subgoals, mimicking chain-of-thought reasoning patterns.
• →The unified architecture with MLP-Mixer backbone outperforms traditional hierarchical methods on navigation and manipulation tasks.
• →Offline reinforcement learning for long-horizon problems becomes more feasible without requiring online interaction.
• →The approach demonstrates that cognitive reasoning principles from language models transfer effectively to robotic control domains.
• →Framework design enables better structural understanding of relationships between state, goals, subgoals, and actions.