Human-Aligned Decision Transformers for smart agriculture microgrid orchestration with zero-trust governance guarantees

Description and use

The article details the deployment of a Human‑Aligned Decision Transformer for agricultural microgrid orchestration, aligning actions with farmer priorities and adding zero‑trust governance.

Problem

A reinforcement‑learning agent that optimizes electricity cost misaligned with farmers’ priorities, causing manual overrides and lost trust.

Outcome

The HADT successfully aligned microgrid actions with farmer priorities, eliminating manual overrides and providing cryptographic guarantees that no single component could unilaterally seize control of physical infrastructure.

Workflow

• 1. Deploy baseline RL scheduler and observe manual overrides.
• 2. Collect operator preference statements (e.g., "tomato harvest this week matters more").
• 3. Use an LLM‑based preference elicitation agent to convert statements into a weight vector.
• 4. Train a preference reward model on pairwise trajectory comparisons from weekly reviews.
• 5. Extend the decision transformer token stream to include return‑to‑go, preference, state, and action at every timestep.
• 6. Inject preferences at every timestep to maintain alignment over long horizons.
• 7. Wrap the transformer inference in a zero‑trust governance layer that cryptographically signs commands and requires multi‑party approval before execution.
• 8. Deploy the HADT in the microgrid and monitor for alignment and safety.
• 9. Iterate preference elicitation and reward‑model fine‑tuning based on operator feedback.

Evidence

the optimizer doesn’t understand that the tomato harvest this week is worth more than the electricity savings it’s chasing

Source

you can steer behavior at inference time simply by changing the target return—no retraining needed

Source

preferences are injected at every timestep, not just at the start

Source