New Two-Stage Framework Boosts Offline Reinforcement Learning in Complex Action Spaces

Global: New Two-Stage Framework Boosts Offline Reinforcement Learning in Complex Action Spaces

Researchers Matthew Landers, Taylor W. Killian, Thomas Hartvigsen, and Afsaneh Doryab released a preprint on January 7, 2026 that proposes Structured Policy Initialization (SPIN), a two‑stage framework aimed at improving offline reinforcement learning (RL) for problems with large discrete combinatorial action spaces.

Background on Offline RL in Discrete Domains

Offline RL seeks to learn effective policies from previously collected data without further environment interaction. In settings where actions consist of multiple sub‑actions that must be combined coherently, the number of possible joint actions grows exponentially, making traditional policy search computationally prohibitive.

Introducing Structured Policy Initialization

SPIN addresses this challenge by first pre‑training an Action Structure Model (ASM) that learns the manifold of valid joint actions. In the second stage, the ASM is frozen and lightweight policy heads are trained on top of the learned representation, allowing the control component to focus on optimization rather than structural discovery.

Methodological Details

The authors implemented the ASM using a neural architecture that captures dependencies among sub‑actions, while the policy heads employ standard RL algorithms adapted for offline data. By decoupling structure learning from control, SPIN reduces the risk of instability that often accompanies joint optimization.

Experimental Evaluation

Benchmarks from the Discrete‑Action DM Control suite were used to compare SPIN against existing state‑of‑the‑art methods. Results showed an average return increase of up to 39% and a reduction in time to convergence of as much as 12.8 times, indicating both higher performance and faster training.

Implications for Future Research

These findings suggest that separating action‑structure learning from policy optimization can be a viable strategy for scaling offline RL to domains such as combinatorial optimization, automated planning, and complex game AI. The authors note that the approach may be extended to other discrete environments with similarly large action spaces.

Limitations and Next Steps

While SPIN demonstrated strong results on synthetic benchmarks, the paper acknowledges the need for validation on real‑world datasets and for exploring how the frozen ASM adapts when the underlying data distribution shifts. Future work is expected to investigate dynamic updating mechanisms for the ASM.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.