# Latency-aware Human-in-the-Loop Reinforcement Learning for Semantic Communications

arXiv: https://arxiv.org/abs/2602.15640 · HTML: https://mlsanity.com/paper/2602.15640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 17 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2504.12501/) (2025)
- [What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence](https://mlsanity.com/paper/2110.00196/) (2021)
- [Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach](https://mlsanity.com/paper/2402.07573/) (2024)
- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)
- [Open RAN meets Semantic Communications: A Synergistic Match for Open, Intelligent, and Knowledge-Driven 6G](https://mlsanity.com/paper/2310.09951/) (2023)
- [Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Management at Open RAN Edges](https://mlsanity.com/paper/2410.23086/) (2024)
- [Task-Oriented Connectivity for Networked Robotics with Generative AI and Semantic Communications](https://mlsanity.com/paper/2503.06771/) (2025)
- [Building the Self-Improvement Loop: Error Detection and Correction in Goal-Oriented Semantic Communications](https://mlsanity.com/paper/2411.01544/) (2024)
- [RL-Driven Semantic Compression Model Selection and Resource Allocation in Semantic Communication Systems](https://mlsanity.com/paper/2506.18660/) (2025)

## Abstract (arXiv)

Semantic communication promises task-aligned transmission but must reconcile semantic fidelity with stringent latency guarantees in immersive and safety-critical services. This paper introduces a time-constrained human-in-the-loop reinforcement learning (TC-HITL-RL) framework that embeds human feedback, semantic utility, and latency control within a semantic-aware Open radio access network (RAN) architecture. We formulate semantic adaptation driven by human feedback as a constrained Markov decision process (CMDP) whose state captures semantic quality, human preferences, queue slack, and channel dynamics, and solve it via a primal--dual proximal policy optimization algorithm with action shielding and latency-aware reward shaping. The resulting policy preserves PPO-level semantic rewards while tightening the variability of both air-interface and near-real-time RAN intelligent controller processing budgets. Simulations over point-to-multipoint links with heterogeneous deadlines show that TC-HITL-RL consistently meets per-user timing constraints, outperforms baseline schedulers in reward, and stabilizes resource consumption, providing a practical blueprint for latency-aware semantic adaptation.