# Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

arXiv: https://arxiv.org/abs/2506.14125 · HTML: https://mlsanity.com/paper/2506.14125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 21 of 21)

- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Projection-Based Constrained Policy Optimization](https://mlsanity.com/paper/2010.03152/) (2020)
- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
- [Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings](https://mlsanity.com/paper/2002.05969/) (2020)
- [Learning to Walk in the Real World with Minimal Human Effort](https://mlsanity.com/paper/2002.08550/) (2020)
- [A Tutorial on Kernel Density Estimation and Recent Advances](https://mlsanity.com/paper/1704.03924/) (2017)
- [Logically-Constrained Reinforcement Learning](https://mlsanity.com/paper/1801.08099/) (2018)
- [Density Constrained Reinforcement Learning](https://mlsanity.com/paper/2106.12764/) (2021)
- [Resource Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/1812.00600/) (2018)
- [On Bayesian index policies for sequential resource allocation](https://mlsanity.com/paper/1601.01190/) (2016)
- [Off-Policy Primal-Dual Safe Reinforcement Learning](https://mlsanity.com/paper/2401.14758/) (2024)
- [Between steps: Intermediate relaxations between big-M and convex hull formulations](https://mlsanity.com/paper/2101.12708/) (2021)
- [Dynamic Shielding for Reinforcement Learning in Black-Box Environments](https://mlsanity.com/paper/2207.13446/) (2022)
- [CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing](https://mlsanity.com/paper/2111.08397/) (2021)
- [Duality between density function and value function with applications in constrained optimal control and Markov Decision Process](https://mlsanity.com/paper/1902.09583/) (2019)
- [Adaptive Sequential Surveillance with Network and Temporal Dependence](https://mlsanity.com/paper/2212.02422/) (2022)
- [Learning Density-Based Correlated Equilibria for Markov Games](https://mlsanity.com/paper/2302.08001/) (2023)
- [Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources](https://mlsanity.com/paper/2309.08560/) (2023)

## Abstract (arXiv)

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.