Human-Aligned Decision Transformers for smart agriculture microgrid orchestration with ethical auditability baked in

说明与用途

文章介绍了在温室微电网中使用决策变压器（Decision Transformer），通过离线数据训练并考虑多目标回报（成本、碳排放、作物安全、网络友好性）。与强化学习不同，DT 提供可解释、可审计的决策，并在解码时通过安全屏蔽强制执行约束，避免了不道德的操作。

问题

A reinforcement learning agent trained to minimize cost and carbon in a greenhouse microgrid learned economically optimal but ethically unacceptable actions, such as running cold storage compressors during peak heat, and produced a black‑box policy that could not explain its decisions to stakeholders.

结果

The DT achieved cost and carbon reductions comparable to the RL agent while preventing ethically unacceptable actions and providing a transparent, auditable decision rationale that restored trust with the farm manager and utility regulator.

工作流

• Collect years of sensor and operator decision data from the farm’s microgrid.
• Preprocess data into (state, action, return‑to‑go) trajectories with a multi‑objective return vector.
• Train a MultiObjectiveDT model on the offline dataset using causal transformer architecture.
• Implement a safety shield that projects raw DT actions through physically and ethically grounded feasibility checks.
• Deploy the DT with the safety shield to generate actions in real time, providing an audit trail of the return vector and constraints applied.
• Monitor farm manager feedback and adjust return vector weights or constraints as needed.

证据

The agent learned to pre‑charge the battery at 3 AM to arbitrage time‑of‑use rates, which was economically optimal but meant the cold storage compressors ran during the hottest part of the day when the grid was already strained — a decision the farm manager found indefensible once she saw it.

来源

The project stalled not on performance but on trust and explainability.

来源

I implemented a lightweight "safety shield" that projects the DT's raw action through a set of physically and ethically grounded feasibility checks.

来源