Addressing Terminal Constraints in Data-Driven Demand Response Scheduling

Calvin Tsay; Ehecatl Antonio del Rio Chanona; Martha White; Maximilian Bloor

REVIEW 2 major objections 1 minor 2 references

Addressing Terminal Constraints in Data-Driven Demand Response Scheduling

T0 review · 2 major / 1 minor · reviewed 2026-06-30 · grok-4.3

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Pith's one-line read Integrating goal-space planning with DDPG lets reinforcement learning satisfy terminal storage constraints in demand response scheduling.

desk verdict This applies GSP to DDPG for terminal constraints in demand response but the abstract supplies no numbers or ablations to show the integration actually works. read the letter →

arxiv 2605.14741 v1 pith:SRTS6EDC submitted 2026-05-14 eess.SY cs.AIcs.SY

Maximilian Bloor , Martha White , Ehecatl Antonio del Rio Chanona , Calvin Tsay This is my paper

classification eess.SYcs.AIcs.SY

keywords demandresponsereinforcementlearningterminalconstraintsgoal-spaceplanningDDPGairseparationscheduling

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper integrates Goal-Space Planning with Deep Deterministic Policy Gradient to handle terminal constraints over long horizons in electrified chemical process scheduling. It relies on learned temporally abstract models over discrete subgoals to propagate value and address credit assignment difficulties that standard RL encounters. In a simulated air separation benchmark the combined approach shows better sample efficiency than plain DDPG while meeting the required terminal storage limits and avoiding myopic behavior. Readers would care because model-based optimization is computationally heavy and conventional data-driven methods often fail to respect stability constraints when electricity prices vary over time.

What carries the argument

Learned temporally abstract models over discrete subgoals within Goal-Space Planning combined with DDPG, which propagate value to enforce terminal constraints over long horizons.

What would settle it

A simulation run of GSP-DDPG on the air separation benchmark that either violates the terminal storage constraints or shows no improvement in sample efficiency over standard DDPG.

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Extended reading notes

Core claim

The GSP-DDPG approach improves sample efficiency over standard DDPG while satisfying terminal storage constraints by using learned temporally abstract models over discrete subgoals to propagate value across extended horizons in the air separation benchmark.

Load-bearing premise

The learned temporally abstract models over discrete subgoals accurately enable value propagation and terminal constraint satisfaction in the air separation benchmark without further validation or error analysis.

Editorial extensions

If this is right

Sample efficiency increases relative to standard DDPG on the benchmark task.
Terminal storage constraints are satisfied while mitigating myopic control.
Value propagates effectively across extended scheduling horizons.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same subgoal abstraction could extend to other chemical processes exposed to time-varying electricity markets.
The method offers a practical alternative when full model-based optimization becomes too slow for real-time scheduling.
Quantifying model error would directly test how reliably the learned subgoals enforce constraints.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

The paper takes goal-space planning and plugs it into DDPG to handle terminal storage constraints over long horizons in demand response scheduling. The central move is learning temporally abstract models over discrete subgoals so value can propagate without the usual credit-assignment problems in standard RL.

What is actually new is the targeted use of that combination on an air separation unit benchmark, where terminal constraints matter for dynamic stability in electrified chemical processes. The framing is clear: model-based optimization is too slow, plain DDPG is myopic, and GSP is meant to fix the horizon issue while keeping the method data-driven.

The paper does a reasonable job stating the practical barrier and sketching how the subgoal models are supposed to enforce the constraints. The benchmark choice is relevant to the domain.

The soft spot is exactly what the stress-test note flags. The abstract asserts better sample efficiency and constraint satisfaction over plain DDPG, yet it gives no quantitative results, no baseline comparisons, no model prediction error, and no ablation isolating the GSP component. Without those, there is no way to tell whether the claimed mechanism is doing the work or whether something else in the implementation is responsible. If the full manuscript has the missing tables and error analysis, that changes the picture; on the supplied description it does not.

This is the sort of incremental application paper that might interest people already working on RL for energy systems or process scheduling. A reader looking for a new core algorithm will not find one. It is worth a serious referee if the experiments turn out to be properly reported and reproducible, because the underlying problem is real even if the current write-up leaves the evidence thin.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes integrating Goal-Space Planning (GSP) with Deep Deterministic Policy Gradient (DDPG) to handle terminal storage constraints in data-driven demand response scheduling for electrified chemical processes. It employs learned temporally abstract models over discrete subgoals to propagate value across long horizons, and reports a demonstration on a simulated air separation benchmark claiming improved sample efficiency over standard DDPG while satisfying the constraints and mitigating myopic behavior.

Significance. If the central claims hold under proper validation, the work could offer a practical data-driven alternative to model-based optimization for long-horizon scheduling under time-varying electricity prices, with potential relevance to flexible operation of chemical plants. The GSP component for abstract value propagation addresses a recognized challenge in RL credit assignment, though the current lack of supporting evidence limits evaluation of its contribution.

major comments (2)

[Demonstration / air separation benchmark] The demonstration section (referenced in the abstract) asserts that GSP-DDPG improves sample efficiency and satisfies terminal constraints on the air separation benchmark, yet supplies no quantitative metrics, baselines, model prediction error, subgoal discretization analysis, constraint violation rates, or ablation studies isolating the abstract models' contribution. This directly undermines verification of the claimed mechanism for value propagation and constraint satisfaction.
[Method / GSP-DDPG integration] The method description provides no details on the learning procedure for the temporally abstract models, the discretization of subgoals, or the precise manner in which terminal constraints are enforced within the GSP-DDPG framework. Without these, it is not possible to assess whether the approach reliably mitigates the myopic behavior described.

minor comments (1)

[Abstract] The abstract would benefit from explicit mention of the performance metrics (e.g., cumulative reward, constraint violation count) used to support the sample-efficiency and constraint-satisfaction claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important areas for strengthening the manuscript. We address each major comment below and will incorporate revisions to improve clarity and evidence.

read point-by-point responses

Referee: [Demonstration / air separation benchmark] The demonstration section (referenced in the abstract) asserts that GSP-DDPG improves sample efficiency and satisfies terminal constraints on the air separation benchmark, yet supplies no quantitative metrics, baselines, model prediction error, subgoal discretization analysis, constraint violation rates, or ablation studies isolating the abstract models' contribution. This directly undermines verification of the claimed mechanism for value propagation and constraint satisfaction.

Authors: We agree that the demonstration would be strengthened by additional quantitative support. The revised manuscript will include tables with sample efficiency metrics (e.g., learning curves and episodes to target performance), comparisons to standard DDPG and other baselines, model prediction errors for the learned abstract models, analysis of subgoal discretization effects, constraint violation rates over episodes, and ablation studies isolating the GSP component's contribution to value propagation and constraint satisfaction. revision: yes
Referee: [Method / GSP-DDPG integration] The method description provides no details on the learning procedure for the temporally abstract models, the discretization of subgoals, or the precise manner in which terminal constraints are enforced within the GSP-DDPG framework. Without these, it is not possible to assess whether the approach reliably mitigates the myopic behavior described.

Authors: We acknowledge that the method section requires expansion for reproducibility and assessment. The revised manuscript will detail the learning procedure for the temporally abstract models (including training objectives and data sources), specify the subgoal discretization method and hyperparameters, and explain the enforcement of terminal constraints (e.g., via modified rewards, constrained optimization, or goal-conditioned value propagation) within the GSP-DDPG framework to clarify mitigation of myopic behavior. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity in derivation chain

full rationale

The paper presents an integration of Goal-Space Planning with DDPG for handling terminal constraints in RL-based scheduling. No equations, self-citations, or fitted parameters are shown that reduce the central claim (improved sample efficiency via temporally abstract models) to a definitional tautology or self-referential fit. The method is described as using learned models to propagate value, with empirical validation on an air separation benchmark; this remains independent of the inputs by construction. No load-bearing self-citation chains or ansatz smuggling appear in the provided text.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract-only information provides no identifiable free parameters, axioms, or invented entities; full manuscript would be needed to audit these.

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Cite this review

Pith. "Pith review of Addressing Terminal Constraints in Data-Driven Demand Response Scheduling." pith.science (2026). https://pith.science/paper/SRTS6EDC

@misc{pith2026260514741,
  author       = {Pith},
  title        = {Pith review of: Addressing Terminal Constraints in Data-Driven Demand Response Scheduling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SRTS6EDC}},
  note         = {Machine review of arXiv:2605.14741}
}

read the original abstract

Electrified chemical processes are incentivized by exposure to time-varying electricity markets to operate flexibly, but participating in demand response schemes can require satisfying terminal constraints over long horizons. Specifically, terminal constraints may be required when computing optimal schedules in order to preserve dynamic stability. Model-based optimization methods are computationally costly, and data-driven scheduling via reinforcement learning (RL) faces severe credit-assignment challenges. We integrate Goal-Space Planning (GSP) with Deep Deterministic Policy Gradient (DDPG), using learned temporally abstract models over discrete subgoals to propagate value across extended horizons. Using a simulated air separation benchmark, we demonstrate the proposed approach improves sample efficiency over standard DDPG while satisfying terminal storage constraints, mitigating myopic control behavior.

Figures

Figures reproduced from arXiv: 2605.14741 by the authors.

**Figure 2.** Illustration of goals in the goal-time space. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Heat map of the learned subgoal values vg∗ using the fully trained goal-to-goal models ˜r(g, g ′ ) and Γ( ˜ g, g ′ ). The heatmap is normalized over each column. Green represents high values and red represents low values. 0 1000 2000 3000 4000 Timestep −25 −20 −15 −10 −5 Episodic Return Offline GSP DDPG Online GSP Online GSP (NP) [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Learning curves for DDPG, GSP with models trained offline (Offline GSP), GSP with models trained online (Online GSP), and GSP without the state-to-goal models (Online GSP (NP)). Algorithms were trained for 80 episodes and repeated over 5 seeds (mean and std. shown). 4…

**Figure 5.** Figure 5: Storage level trajectories during training (Episodes [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 6.** Figure 6: Distribution of final storage levels at episodes 40 and 80 for Offline GSP, Online GSP, and DDPG across 5 seeds. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

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Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages

[1]

Baldea, M., Endler, E.E., Hale, E., Maravelias, C.T., Barolo, M., Harjunkoski, I., Mercangoz, M., Shah, S.L., Soroush, M., Young, B.R., et al. (2025). Transforming the process industries through electrification: Challenges and opportunities.Ind. Eng. Chem. Res., 64(34), 16466– 16478. Bloor, M., Ahmed, A., Kotecha, N., Mercangoz, M., Tsay, C., and del Rio-...

work page Pith review arXiv 2025
[2]

Tsay, C., Kumar, A., Flores-Cerrillo, J., and Baldea, M

Doi:10.17632/pfcc5gvzty.1. Tsay, C., Kumar, A., Flores-Cerrillo, J., and Baldea, M. (2019). Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models.Comput. Chem. Eng., 126, 22–34. Yoo, H., Byun, H.E., Han, D., and Lee, J.H. (2021). Re- inforcement learning for batch process control: Review and perspectives....

work page doi:10.17632/pfcc5gvzty.1 2019

Pith tools

Reviewed June 30, 2026 · model on record in the stance chip above.

[1] [1]

Baldea, M., Endler, E.E., Hale, E., Maravelias, C.T., Barolo, M., Harjunkoski, I., Mercangoz, M., Shah, S.L., Soroush, M., Young, B.R., et al. (2025). Transforming the process industries through electrification: Challenges and opportunities.Ind. Eng. Chem. Res., 64(34), 16466– 16478. Bloor, M., Ahmed, A., Kotecha, N., Mercangoz, M., Tsay, C., and del Rio-...

work page Pith review arXiv 2025

[2] [2]

Tsay, C., Kumar, A., Flores-Cerrillo, J., and Baldea, M

Doi:10.17632/pfcc5gvzty.1. Tsay, C., Kumar, A., Flores-Cerrillo, J., and Baldea, M. (2019). Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models.Comput. Chem. Eng., 126, 22–34. Yoo, H., Byun, H.E., Han, D., and Lee, J.H. (2021). Re- inforcement learning for batch process control: Review and perspectives....

work page doi:10.17632/pfcc5gvzty.1 2019