# CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing

arXiv: https://arxiv.org/abs/2111.08397 · HTML: https://mlsanity.com/paper/2111.08397/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [Risk-Constrained Reinforcement Learning with Percentile Risk Criteria](https://mlsanity.com/paper/1512.01629/) (2015)
- [Safe Exploration in Continuous Action Spaces](https://mlsanity.com/paper/1801.08757/) (2018)
- [IPO: Interior-point Policy Optimization under Constraints](https://mlsanity.com/paper/1910.09615/) (2019)
- [Deep Reinforcement Learning for Resource Management in Network Slicing](https://mlsanity.com/paper/1805.06591/) (2018)
- [Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning](https://mlsanity.com/paper/1805.06146/) (2018)
- [Resource Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/1812.00600/) (2018)
- [Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks](https://mlsanity.com/paper/1801.02850/) (2018)
- [Network Slicing to Enable Scalability and Flexibility in 5G Mobile Networks](https://mlsanity.com/paper/1704.02129/) (2017)
- [The Slice Is Served: Enforcing Radio Access Network Slicing in Virtualized 5G Systems](https://mlsanity.com/paper/1902.00389/) (2019)
- [MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design](https://mlsanity.com/paper/1909.09063/) (2019)

## Built on by (most cited first, 7 of 7)

- [Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency](https://mlsanity.com/paper/2207.11386/) (2022)
- [Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learning](https://mlsanity.com/paper/2503.20139/) (2025)
- [SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning](https://mlsanity.com/paper/2503.12753/) (2025)
- [Adventurer: Exploration with BiGAN for Deep Reinforcement Learning](https://mlsanity.com/paper/2503.18612/) (2025)
- [Towards Fully Automated Decision-Making Systems for Greenhouse Control: Challenges and Opportunities](https://mlsanity.com/paper/2503.21640/) (2025)
- [Situational-Constrained Sequential Resources Allocation via Reinforcement Learning](https://mlsanity.com/paper/2506.14125/) (2025)
- [Joint User Priority and Power Scheduling for QoS-Aware WMMSE Precoding: A Constrained-Actor Attentive-Critic Approach](https://mlsanity.com/paper/2506.16074/) (2025)

## Abstract (arXiv)

As mobile networks proliferate, we are experiencing a strong diversification of services, which requires greater flexibility from the existing network. Network slicing is proposed as a promising solution for resource utilization in 5G and future networks to address this dire need. In network slicing, dynamic resource orchestration and network slice management are crucial for maximizing resource utilization. Unfortunately, this process is too complex for traditional approaches to be effective due to a lack of accurate models and dynamic hidden structures. We formulate the problem as a Constrained Markov Decision Process (CMDP) without knowing models and hidden structures. Additionally, we propose to solve the problem using CLARA, a Constrained reinforcement LeArning based Resource Allocation algorithm. In particular, we analyze cumulative and instantaneous constraints using adaptive interior-point policy optimization and projection layer, respectively. Evaluations show that CLARA clearly outperforms baselines in resource allocation with service demand guarantees.