Pith. sign in

REVIEW 4 major objections 3 minor 49 references

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

T0 review · 4 major / 3 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read An RL agent can satisfy conditional allocation rules by turning each if-then constraint into a dynamic penalty and randomly choosing which side of the disjunction to enforce.

desk verdict A sound but incremental extension of density-constrained RL to conditional constraints; the new disjunctive selection mechanism is heuristic and weakly supported. read the letter →

arxiv 2506.14125 v1 pith:MV2KCLBP submitted 2025-06-17 cs.AI

Libo Zhang , Yang Chen , Toru Takisaka , Kaiqi Zhao , Weidong Li , Jiamou Liu This is my paper · ORCID
classification cs.AI
keywords sequentialresourceallocationsituationalconstraintsconstrainedreinforcementlearningdensity-constraineddisjunctivepunitivetermequityandadequacy
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

Sequential resource allocation often comes with context-dependent rules: if some regions cannot get enough, then others must be treated equally; if one region gets more than a threshold, another must receive a minimum. The paper claims that such situational constraints can be converted into dynamic reward penalties, so a reinforcement learning agent learns to satisfy them without solving a mixed-integer program. The proposed SCRL algorithm keeps a running penalty factor for each atomic constraint, penalizes each state in proportion to its contribution to the violation, and handles the logical "or" in an implication by randomly picking a disjunct with probability inversely proportional to its penalty factor. In simulated medical and agricultural scenarios, SCRL reports near-zero constraint violations on situational and priority tasks, and it beats the RCPO, CAL, and DCRL baselines on constraint satisfaction while keeping reward costs moderate.

What carries the argument

The load-bearing object is the punitive term $\sigma(\varphi,s)=w(\varphi,s)\kappa(\varphi)$ applied to the reward at each state. $\kappa(\varphi)$ is a penalty factor that grows by $\beta\cdot Vio_\pi(\varphi)$ when the current policy violates $\varphi$, where $Vio_\pi(\varphi)=\mathbf{a}\cdot\rho_\pi - b$ for an atomic constraint $\mathbf{a}\cdot\rho \le b$, and decays otherwise. $w(\varphi,s)=(\sum_i a_i \mathbf{1}_{i\in L(s)})/(\sum_i |a_i|)$ attributes the penalty to the regions the agent is visiting. For a disjunction, the algorithm selects a disjunct with probability $p_j \propto \kappa(\varphi_j)^{-1}$, treating the selected disjunct as the active constraint; this keeps badly violated disjuncts in play instead of letting a greedy min-operator fixate on an infeasible but superficially easy branch. The whole construction converts a constrained MDP with conditional constraints into a sequence of ordinary reward-maximization updates.

What would settle it

Build a two-region MDP whose constraint is $\varphi_1 \lor \varphi_2$, with $\varphi_1$ infeasible for every policy yet carrying a permanently smaller penalty factor than the feasible $\varphi_2$; if SCRL keeps choosing $\varphi_1$ most of the time and the learned policy violates $\varphi_2$, the central claim is false. The paper's own min-operator discussion shows such a configuration is the known failure mode, so this directly tests whether the probabilistic mechanism escapes it.

Watch

Extended reading notes

Core claim

The paper's central claim is that situational constraints, formalized as implications $\varphi_1(\rho) \to \varphi_2(\rho)$ over expected allocation densities, can be enforced inside density-constrained reinforcement learning, which the authors identify as the first treatment of situational, disjunctive constraints in the CRL paradigm. The mechanism is a punitive term $\sigma(\varphi,s)=w(\varphi,s)\kappa(\varphi)$: $\kappa$ grows when the policy violates $\varphi$ and shrinks when it satisfies it, while $w$ distributes the penalty over states by how much each state contributes to the violation. For a disjunction $\neg\varphi_1 \lor \varphi_2$, SCRL does not greedily penalize the least-violated disjunct, which can strand the policy on an infeasible branch, but instead samples a disjunct with probability proportional to the inverse of its penalty factor. Empirically, the claim is that this yields policies with near-zero constraint violation on priority and situational tasks in two real-world-inspired simulations, with joint-task violations kept low while reward stays competitive.

Load-bearing premise

The load-bearing premise is that randomly selecting a disjunct with probability inversely proportional to its current penalty factor will, over training, steer the policy into the feasible set of a disjunctive constraint; this is a heuristic whose convergence is proven only for atomic conjunctive constraints, not for disjunctions.

Editorial extensions

If this is right

  • "If-then" allocation policies can be learned directly from interaction, without reformulating each implication as a mixed-integer program or maintaining explicit logical state.
  • Density-constrained RL, which already enforces interval and equity constraints, gains a mechanism for switching between constraint regimes as the situation changes.
  • A single policy can fall back to equity when resources are short and switch to adequacy when resources are ample, using the same punitive-term machinery.
  • The cost of satisfying situational constraints is explicit and tunable through the penalty learning rate, so fairness can be traded against efficiency in a controlled way.
  • The near-zero violations reported on situational and priority tasks suggest the same disjunct-sampling mechanism can be composed across multiple conditional rules, though the paper only demonstrates up to four disjuncts in a case study.

Reading between the lines

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

  • The probabilistic selection is effectively exploration over the logical structure of a constraint; analyzing its regret or convergence rate on a two-disjunct MDP would be a natural next step, since the paper proves convergence only for atomic constraints.
  • If the constant-rate dispensing assumption is relaxed so the agent controls release rates, the same penalties would need to weight dwell time by rate; that is a direct extension rather than a change of framework.
  • Although the paper limits itself to a single allocator and a single resource type, the additive structure of the punitive term suggests it could attach to multi-agent density-constrained methods, where equity constraints are already expressed as density equalities.
  • The ablation result, where the min-operator variant shows slightly higher violations, supports the paper's diagnosis of infeasible-disjunct traps; a task engineered so the infeasible disjunct always carries the smaller penalty would isolate that failure mode.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 3 minor

Summary. The paper introduces SCRL, a constrained reinforcement learning framework for sequential resource allocation under what it calls situational constraints, formalized as logical implications between affine constraints on state-visitation densities. The proposed method extends density-constrained RL by defining a punitive reward term that combines a per-constraint penalty factor, updated only when the constraint is violated, with a state-level weighting factor. For disjunctive constraints, which arise from the rewriting of an implication as a disjunction, the paper proposes a probabilistic mechanism that selects one atomic disjunct with probability inversely proportional to its current penalty factor. The manuscript reports experiments on two scenarios, pandemic medical-resource allocation and agricultural pesticide distribution, comparing SCRL against DDPG, RCPO, CAL, and DCRL, and reports near-zero constraint violations on situational and priority tasks.

Significance. If the central claims hold, the paper would be a useful extension of density-constrained CRL to conditional or disjunctive constraints, a class that is indeed not addressed by standard Lagrangian CRL methods. The reduction of an atomic constraint to a canonical CRL cost function in Proposition 1 is mathematically clean and appears correct under standard Lagrangian convergence assumptions; this is a solid building block. The problem formulation itself, using discounted occupancy densities as resource amounts, is coherent and well motivated. However, the paper's distinguishing contribution, the probabilistic disjunct-selection mechanism, has no formal guarantee and is supported only by a small, statistically inconclusive ablation, so the significance of the claimed novelty is currently conditional rather than established.

major comments (4)
  1. [Section 4.3 and Algorithm 3] The probabilistic disjunct-selection mechanism is the central novelty of the paper, but the paper provides no convergence or feasibility guarantee for it. Proposition 1 covers only a single atomic constraint of the form a·rho <= b, and its proof in Appendix 7.1 explicitly reduces that case to a canonical Lagrangian CRL problem; it says nothing about unions of feasible sets or about the random variable Phi defined in Section 4.3. The expected penalty induced by Algorithm 3 is sum_j w(phi_j,s) / sum_j kappa(phi_j)^(-1), which is not the violation degree of any single logical disjunct and has no demonstrated fixed-point or feasibility relationship with the disjunctive constraint. To make the load-bearing claim defensible, the authors should either prove that the stochastic penalty dynamics steer the policy into the feasible set of the disjunction under stated conditions (e.g., a Slater-type condition), or explicitly reframe the mechanism as a heuristic and provide substantially stronger empirical evidence for it. The current text presents it as a principled solution without support.
  2. [Section 5.7, Table 4] The ablation study does not establish that the probabilistic mechanism is responsible for SCRL's performance. On the Med. situational task, SCRL-min achieves 0.7 +/- 2.3 versus 0.0 +/- 0.0 for SCRL, and on the Agri. situational task it achieves 0.3 +/- 0.8 versus 0.0 +/- 0.0; with standard deviations larger than the mean differences and only 10 independent runs, the comparison is statistically inconclusive. A paired statistical test, more seeds, or a direct evaluation on the infeasible-disjunct scenario described in Figure 5 is needed before the mechanism can be credited with the reported advantage.
  3. [Section 3.1 versus Appendix 7.3, Table 5] The formal definition of a situational constraint in Definition 1 is phi1 -> phi2 with phi1 and phi2 atomic constraints of the form a·f <= b, but several experimental task specifications do not fit this definition. For example, Agri.-Situational is written as not(rho(2) <= 300) -> rho(3) >= 800 and Agri.-Priority has a negated antecedent not(phi1 and phi2 and phi3); a negated atomic formula is not an atomic constraint under Definition 1. Even if the conclusion rho(3) >= 800 can be encoded as an atomic constraint by multiplying by -1, the negated antecedent cannot be represented as a single atomic constraint of the defined form. The authors should either extend Definition 1 to allow arbitrary Boolean combinations of atomic constraints, or show explicitly how each task in Table 5 is rewritten into the defined problem class. As written, the experiments are conducted on a broader problem class than the one formalized in Section 3.
  4. [Section 3.2 and Problem 1] The identification of the allocated resource amount f(i) with the discounted dwell time rho_tau(i) relies on the constant-rate dispensing assumption. The paper states this assumption once in Section 3.2, but it does not discuss whether the two experimental scenarios satisfy it, nor does it explain how a variable release rate would affect the constraint semantics. Since all constraints in Problem 1 are expressed over rho_pi(i), this assumption is load-bearing for the problem formulation and should be stated as an explicit modeling assumption with its scope and limitations.
minor comments (3)
  1. [Algorithm 2, line 1] The input notation 'Phi := V_i W_{j in {1,2}} phi_j' appears to use V and W where logical conjunction and disjunction are intended; please replace these with the standard symbols or clarify the intended operators.
  2. [Section 5.3] The phrase 'higher rewards indicating less resource allocation amount' is confusing because the reward function is described as negative; please define the mapping from reward to resource usage more precisely so that the sign convention is unambiguous.
  3. [Section 5.6, Table 3] In the multi-disjunct case study, DCRL3 and DCRL4 also achieve zero constraint violation, so the text should explain more concretely why SCRL is preferred over those DCRL variants beyond the reported reward difference, especially given the large reward spread among DCRL runs.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: constraints are exogenous, the atomic punitive mechanism is an explicit Lagrangian reduction, and the disjunctive heuristic is unsupported but not derived from its own conclusion.

full rationale

The paper's derivation chain does not reduce any central claim to its inputs by construction. Atomic constraints are specified by the task definitions, not fitted to the algorithm's outputs. The punitive mechanism defines violation degree Vio_pi(phi) = a·rho - b, updates kappa by kappa' = max(0, kappa + beta·Vio_pi(phi)), and sets the penalized reward r' = r - sigma(phi,s). Proposition 1 explicitly reduces this to canonical Lagrangian CRL: the proof rewrites r' = r - lambda(phi)·c_phi(s) with c_phi(s) = sum_i a_i·1_{i in L(s)} and shows that kappa/d behaves as a Lagrange multiplier updated by dual ascent. This is a standard reduction to a known convergence result, not a self-definitional equivalence. The situational-constraint mechanism for disjunctions is a heuristic: Algorithm 3 selects a disjunct with probability p_j proportional to kappa(phi_j)^{-1}, and the paper provides no convergence or feasibility guarantee for this selection. That is a missing proof and a correctness risk, but it is not circular, because the disjunctive penalty is not defined in terms of the target feasibility property and the experimental constraints are not derived from the mechanism. The paper cites the authors' prior work [Zhang et al., 2023] only as background for multi-agent density constraints and equity formulations; that citation is not load-bearing for the situational or disjunctive contribution, and no uniqueness theorem is imported from the authors' own prior work. Overall, the central claims are independently supported by the Lagrangian reduction and the empirical comparisons, so there is no significant circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim depends on the reward scaling, the penalty learning rate, and the density definition as free or inconsistent choices. The learning rate beta is the key free parameter for the punitive mechanism. The domain assumptions about constant-rate dispensing and discounted density are inherited from DCRL but are load-bearing for translating dwell time into resource amounts.

free parameters (3)
  • beta (penalty learning rate) = not reported
    Controls the update step of the penalty factor kappa in Algorithm 2; no value or tuning procedure is given, and the convergence argument in Proposition 1 requires it to be sufficiently small.
  • reward scale constant = 0.1
    Defines the reward as r_t = -0.1/(1+v_t); it is chosen without sensitivity analysis, and its magnitude relative to the punitive term affects the reward-constraint trade-off.
  • effective discount for density = gamma = 1 (undiscounted) in experiments
    Theory defines expected density with gamma in (0,1) while Section 5.3 uses undiscounted finite-horizon sums; this inconsistency is not discussed.
assumptions (4)
  • domain assumption The agent distributes resources at a constant rate while moving, so allocation equals dwell time (Section 3.2).
    This links the density function to the trajectory but fails if the dispensing rate varies.
  • standard math Convergence of canonical Lagrangian CRL (Tessler et al. 2018) for atomic constraints (Appendix 7.1).
    The proof of Proposition 1 reduces the atomic case to a Lagrangian CRL and borrows its convergence; the signed cost issue from negative weights is not addressed.
  • domain assumption Empirical density is an undiscounted finite-horizon average (Section 5.3).
    The experiments evaluate constraints on trajectory samples, but the theory requires expected discounted densities; the approximation is not formally justified.
  • domain assumption Feasibility of the constrained policy set for the tested tasks except where noted (Section 5.1).
    The paper states that joint tasks may be infeasible, yet it uses violation values as a fairness comparison without a feasibility analysis.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Situational-Constrained Sequential Resources Allocation via Reinforcement Learning." pith.science (2026). https://pith.science/paper/MV2KCLBP

@misc{pith2026250614125,
  author       = {Pith},
  title        = {Pith review of: Situational-Constrained Sequential Resources Allocation via Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MV2KCLBP}},
  note         = {Machine review of arXiv:2506.14125}
}
read the original abstract

Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications, where resource demands and priorities are context-dependent. This paper introduces a novel framework, SCRL, to address this problem. We formalize situational constraints as logic implications and develop a new algorithm that dynamically penalizes constraint violations. To handle situational constraints effectively, we propose a probabilistic selection mechanism to overcome limitations of traditional constraint reinforcement learning (CRL) approaches. We evaluate SCRL across two scenarios: medical resource allocation during a pandemic and pesticide distribution in agriculture. Experiments demonstrate that SCRL outperforms existing baselines in satisfying constraints while maintaining high resource efficiency, showcasing its potential for real-world, context-sensitive decision-making tasks.

Figures

Figures reproduced from arXiv: 2506.14125 by the authors.

Figure 1
Figure 1. Left: Simulated medical resource demand in Beijing, where darker colors represent higher demand levels. The experi￾ment focuses on a district in the southwest. Right: Farmland in Saskatchewan, Canada, used for pesticide allocation. Numbers indi￾cate regions with varying pesticide requirements. 2023] to adjust policies according to the level of emergency. E.g. one-meter distancing measures were only enforced when pub… view at source ↗
Figure 2
Figure 2. The sub-regions in two scenarios. For both scenarios, the [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. SCRL’s resource allocation as heatmap. Higher temprera [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Example of representing ψ. The number is the value of σ(ψi,j ), and boolean operators are represented by arithmetical op￾erators. the cumulative punished reward E[ P t γ t r ′ (st) | η, P]. Un￾der standard assumptions, the algorithm converges to a fea￾sible solution of…
Figure 5
Figure 5. Figure 5: Illustration of sub-optimality in taking [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: SCRL’s resources allocation as heatmap. In the heatmaps, [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

49 extracted references · 38 canonical work pages

  1. [1]

    Constrained policy optimization

    Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel. Constrained policy optimization. In International conference on machine learning , pages 22--31. PMLR, 2017

    work page 2017

  2. [2]

    Safe reinforcement learning via shielding, 2017

    Mohammed Alshiekh, Roderick Bloem, Ruediger Ehlers, Bettina Könighofer, Scott Niekum, and Ufuk Topcu. Safe reinforcement learning via shielding, 2017

    work page 2017

  3. [3]

    Asymptotic properties of constrained markov decision processes

    Eitan Altman. Asymptotic properties of constrained markov decision processes. Zeitschrift f \"u r Operations Research , 37(2):151--170, 1993

    work page 1993

  4. [4]

    Deep reinforcement learning for demand response in distribution networks

    Shahab Bahrami, Yu Christine Chen, and Vincent WS Wong. Deep reinforcement learning for demand response in distribution networks. IEEE Transactions on Smart Grid , 12(2):1496--1506, 2020

    work page 2020

  5. [5]

    Dynamic allocations for multi-product distribution

    Yehuda Bassok and Ricardo Ernst. Dynamic allocations for multi-product distribution. Transportation Science , 29(3):256--266, 1995

    work page 1995

  6. [6]

    Deliveries in an inventory/routing problem using stochastic dynamic programming

    Oded Berman and Richard C Larson. Deliveries in an inventory/routing problem using stochastic dynamic programming. Transportation Science , 35(2):192--213, 2001

    work page 2001

  7. [7]

    Resource constrained deep reinforcement learning

    Abhinav Bhatia, Pradeep Varakantham, and Akshat Kumar. Resource constrained deep reinforcement learning. In Proceedings of the International Conference on Automated Planning and Scheduling , volume 29, pages 610--620, 2019

    work page 2019

  8. [8]

    Duality between density function and value function with applications in constrained optimal control and Markov Decision Process

    Yuxiao Chen and Aaron D Ames. Duality between density function and value function with applications in constrained optimal control and markov decision process. arXiv preprint arXiv:1902.09583 , 2019

    work page Pith review arXiv 1902

Show all 49 references
  1. [9]

    A tutorial on kernel density estimation and recent advances

    Yen-Chi Chen. A tutorial on kernel density estimation and recent advances. Biostatistics & Epidemiology , 1(1):161--187, 2017

    2017

  2. [10]

    Supervised fuzzy reinforcement learning for robot navigation

    Fatemeh Fathinezhad, Vali Derhami, and Mehdi Rezaeian. Supervised fuzzy reinforcement learning for robot navigation. Applied Soft Computing , 40:33--41, 2016

    2016

  3. [11]

    A comprehensive survey on safe reinforcement learning

    Javier Garc a and Fernando Fern \'a ndez. A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research , 16(1):1437--1480, 2015

    2015

  4. [12]

    Fuzzy q-learning

    Pierre Yves Glorennec and Lionel Jouffe. Fuzzy q-learning. In Proceedings of 6th international fuzzy systems conference , volume 2, pages 659--662. IEEE, 1997

    1997

  5. [13]

    Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates

    Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In 2017 IEEE international conference on robotics and automation (ICRA) , pages 3389--3396. IEEE, 2017

    2017

  6. [14]

    A review of safe reinforcement learning: Methods, theory and applications

    Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, Yaodong Yang, and Alois Knoll. A review of safe reinforcement learning: Methods, theory and applications. arXiv preprint arXiv:2205.10330 , 2022

    2022 arXiv

  7. [15]

    Learning to walk in the real world with minimal human effort

    Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, and Jie Tan. Learning to walk in the real world with minimal human effort. arXiv preprint arXiv:2002.08550 , 2020

    2002 arXiv

  8. [16]

    Hierarchical reinforcement learning for scarce medical resource allocation with imperfect information

    Qianyue Hao, Fengli Xu, Lin Chen, Pan Hui, and Yong Li. Hierarchical reinforcement learning for scarce medical resource allocation with imperfect information. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , pages 2955--2963, 2021

    2021

  9. [17]

    Logically-constrained reinforcement learning

    Mohammadhosein Hasanbeig, Alessandro Abate, and Daniel Kroening. Logically-constrained reinforcement learning. arXiv preprint arXiv:1801.08099 , 2018

    2018 arXiv

  10. [18]

    Deep reinforcement learning with temporal logics

    Mohammadhosein Hasanbeig, Daniel Kroening, and Alessandro Abate. Deep reinforcement learning with temporal logics. In International Conference on Formal Modeling and Analysis of Timed Systems , pages 1--22. Springer, 2020

    2020

  11. [19]

    Achieving sustainable supply chains through energy justice

    Raphael J Heffron and Darren McCauley. Achieving sustainable supply chains through energy justice. Applied Energy , 123:435--437, 2014

    2014

  12. [20]

    Line: Logical query reasoning over hierarchical knowledge graphs

    Zijian Huang, Meng-Fen Chiang, and Wang-Chien Lee. Line: Logical query reasoning over hierarchical knowledge graphs. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages 615--625, 2022

    2022

  13. [21]

    America's strategy to secure the supply chain for a robust clean energy transition

    Tsisilile Igogo. America's strategy to secure the supply chain for a robust clean energy transition. Technical report, USDOE Office of Policy, Washington DC (United States), 2022

    2022

  14. [22]

    Community-based operations research

    Michael P Johnson and Karen Smilowitz. Community-based operations research. In OR tools and applications: Glimpses of future technologies , pages 102--123. INFORMS, 2007

    2007

  15. [23]

    On bayesian index policies for sequential resource allocation

    Emilie Kaufmann. On bayesian index policies for sequential resource allocation. The Annals of Statistics , 46(2):842--865, 2018

    2018

  16. [24]

    Between steps: Intermediate relaxations between big-m and convex hull formulations

    Jan Kronqvist, Ruth Misener, and Calvin Tsay. Between steps: Intermediate relaxations between big-m and convex hull formulations. In International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research , pages 299--314. Springer, 2021

    2021

  17. [25]

    Augmenting neural networks with first-order logic

    Tao Li and Vivek Srikumar. Augmenting neural networks with first-order logic. arXiv preprint arXiv:1906.06298 , 2019

    1906 arXiv

  18. [26]

    Deep reinforcement learning for efficient and fair allocation of health care resources

    Yikuan Li, Chengsheng Mao, Kaixuan Huang, Hanyin Wang, Zheng Yu, Mengdi Wang, and Yuan Luo. Deep reinforcement learning for efficient and fair allocation of health care resources. arXiv preprint arXiv:2309.08560 , 2023

    2023 arXiv

  19. [27]

    Sequential resource allocation for nonprofit operations

    Robert W Lien, Seyed MR Iravani, and Karen R Smilowitz. Sequential resource allocation for nonprofit operations. Operations Research , 62(2):301--317, 2014

    2014

  20. [28]

    Clara: A constrained reinforcement learning based resource allocation framework for network slicing

    Yongshuai Liu, Jiaxin Ding, Zhi-Li Zhang, and Xin Liu. Clara: A constrained reinforcement learning based resource allocation framework for network slicing. In 2021 IEEE International Conference on Big Data (Big Data) , pages 1427--1437. IEEE, 2021

    2021

  21. [29]

    Adaptive sequential surveillance with network and temporal dependence

    Ivana Malenica, Jeremy R Coyle, Mark J van der Laan, and Maya L Petersen. Adaptive sequential surveillance with network and temporal dependence. Biometrics , 80(1):ujad007, 2024

    2024

  22. [30]

    Ethical resource allocation in policing: Why policing requires a different approach from healthcare

    Hannah Maslen and Colin Paine. Ethical resource allocation in policing: Why policing requires a different approach from healthcare. Criminal Justice Ethics , 43(1):1--36, 2024

    2024

  23. [31]

    A primal dual formulation for deep learning with constraints

    Yatin Nandwani, Abhishek Pathak, and Parag Singla. A primal dual formulation for deep learning with constraints. Advances in neural information processing systems , 32, 2019

    2019

  24. [32]

    Deep reinforcement learning approach for capacitated supply chain optimization under demand uncertainty

    Zedong Peng, Yi Zhang, Yiping Feng, Tuchao Zhang, Zhengguang Wu, and Hongye Su. Deep reinforcement learning approach for capacitated supply chain optimization under demand uncertainty. In 2019 Chinese automation congress (CAC) , pages 3512--3517. IEEE, 2019

    2019

  25. [33]

    Combining fuzzy logic and reinforcement learning for resource management in edge computing

    S Ram Prasath, M Selvi, R Santhana Krishnan, Ezhil E Nithila, P Stella Rose Malar, and G Ram Sankar. Combining fuzzy logic and reinforcement learning for resource management in edge computing. In 2024 5th International Conference on Electronics and Sustainable Communication Sy...

    2024

  26. [34]

    Fairness of the distribution of public medical and health resources

    Lida Pu. Fairness of the distribution of public medical and health resources. Frontiers in public health , 9:768728, 2021

    2021

  27. [35]

    Density constrained reinforcement learning

    Zengyi Qin, Yuxiao Chen, and Chuchu Fan. Density constrained reinforcement learning. In International Conference on Machine Learning , pages 8682--8692. PMLR, 2021

    2021

  28. [36]

    A dual to lyapunov's stability theorem

    Anders Rantzer. A dual to lyapunov's stability theorem. Systems & Control Letters , 42(3):161--168, 2001

    2001

  29. [37]

    Benchmarking Safe Exploration in Deep Reinforcement Learning

    Alex Ray, Joshua Achiam, and Dario Amodei. Benchmarking Safe Exploration in Deep Reinforcement Learning . 2019

    2019

  30. [38]

    Query2box: Reasoning over knowledge graphs in vector space using box embeddings

    Hongyu Ren, Weihua Hu, and Jure Leskovec. Query2box: Reasoning over knowledge graphs in vector space using box embeddings. arXiv preprint arXiv:2002.05969 , 2020

    2002 arXiv

  31. [39]

    Apprenticeship learning using linear programming

    Umar Syed, Michael Bowling, and Robert E Schapire. Apprenticeship learning using linear programming. In Proceedings of the 25th international conference on Machine learning , pages 1032--1039, 2008

    2008

  32. [40]

    The impacts of the covid-19 traffic light system on staff in tertiary education in new zealand

    Lee-Anne Taylor, Jodee Reid, and Anita Jagroop-Dearing. The impacts of the covid-19 traffic light system on staff in tertiary education in new zealand. Education Sciences , 14(1):48, 2023

    2023

  33. [41]

    Reward constrained policy optimization

    Chen Tessler, Daniel J Mankowitz, and Shie Mannor. Reward constrained policy optimization. arXiv preprint arXiv:1805.11074 , 2018

    2018 arXiv

  34. [42]

    Improved big-m reformulation for generalized disjunctive programs

    Francisco Trespalacios and Ignacio E Grossmann. Improved big-m reformulation for generalized disjunctive programs. Computers & Chemical Engineering , 76:98--103, 2015

    2015

  35. [43]

    Disjunctive programming techniques for the optimization of process systems with discontinuous investment costs- multiple size regions

    Metin T \"u rkay and Ignacio E Grossmann. Disjunctive programming techniques for the optimization of process systems with discontinuous investment costs- multiple size regions. Industrial & engineering chemistry research , 35(8):2611--2623, 1996

    1996

  36. [44]

    Dynamic shielding for reinforcement learning in black-box environments, 2022

    Masaki Waga, Ezequiel Castellano, Sasinee Pruekprasert, Stefan Klikovits, Toru Takisaka, and Ichiro Hasuo. Dynamic shielding for reinforcement learning in black-box environments, 2022

    2022

  37. [45]

    Off-policy primal-dual safe reinforcement learning

    Zifan Wu, Bo Tang, Qian Lin, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, and Dong Wang. Off-policy primal-dual safe reinforcement learning. arXiv preprint arXiv:2401.14758 , 2024

    2024 arXiv

  38. [46]

    Projection-based constrained policy optimization

    Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, and Peter J Ramadge. Projection-based constrained policy optimization. arXiv preprint arXiv:2010.03152 , 2020

    2010 arXiv

  39. [47]

    Learning density-based correlated equilibria for markov games

    Libo Zhang, Yang Chen, Toru Takisaka, Bakh Khoussainov, Michael Witbrock, and Jiamou Liu. Learning density-based correlated equilibria for markov games. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems , pages 652--660, 2023

    2023

  40. [48]

    The energy injustice of hydropower: Development, resettlement, and social exclusion at the hongjiang and wanmipo hydropower stations in china

    Xiaofan Zhao, Liang Wu, and Ye Qi. The energy injustice of hydropower: Development, resettlement, and social exclusion at the hongjiang and wanmipo hydropower stations in china. Energy Research & Social Science , 62:101366, 2020

    2020

  41. [49]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence '...

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.