# State-Augmented Graphs for Circular Economy Triage

arXiv: https://arxiv.org/abs/2512.15824 · HTML: https://mlsanity.com/paper/2512.15824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs](https://mlsanity.com/paper/2110.05038/) (2021)
- [Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation](https://mlsanity.com/paper/2202.06558/) (2022)
- [State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards](https://mlsanity.com/paper/2102.11941/) (2021)
- [On the Complexity of Solving Markov Decision Problems](https://mlsanity.com/paper/1302.4971/) (2013)
- [Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation](https://mlsanity.com/paper/2006.13189/) (2020)
- [Learning Safe Policies with Expert Guidance](https://mlsanity.com/paper/1805.08313/) (2018)
- [A State Augmentation based approach to Reinforcement Learning from Human Preferences](https://mlsanity.com/paper/2302.08734/) (2023)

## Abstract (arXiv)

Circular economy (CE) triage is the assessment of products to determine which sustainable pathway they can follow once they reach the end of their usefulness as they are currently being used. Effective CE triage requires adaptive decisions that balance retained value against the costs and constraints of processing and labour. This paper presents a novel decision-making framework as a simple deterministic solver over a state-augmented Disassembly Sequencing Planning (DSP) graph. By encoding the disassembly history into the state, our framework enforces the Markov property, enabling optimal, recursive evaluation by ensuring each decision only depends on the previous state. The triage decision involves choices between continuing disassembly or committing to a CE option. The model integrates condition-aware utility based on diagnostic health scores and complex operational constraints. We demonstrate the framework's flexibility with a worked example: the hierarchical triage of electric vehicle (EV) batteries, where decisions are driven by the recursive valuation of components. The example illustrates how a unified formalism enables the accommodation of varying mechanical complexity, safety requirements, and economic drivers. This unified formalism therefore provides a tractable and generalisable foundation for optimising CE triage decisions across diverse products and operational contexts.