mlsanityData to 7 Oct 2026

Symbolic Reinforcement Learning for Safe RAN Control

arXiv 2103.06602 ↗First posted 11 Mar 2021cs.AIcited by 4

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Abstract (arXiv)

In this paper, we demonstrate a Symbolic Reinforcement Learning (SRL) architecture for safe control in Radio Access Network (RAN) applications. In our automated tool, a user can select a high-level safety specifications expressed in Linear Temporal Logic (LTL) to shield an RL agent running in a given cellular network with aim of optimizing network performance, as measured through certain Key Performance Indicators (KPIs). In the proposed architecture, network safety shielding is ensured through model-checking techniques over combined discrete system models (automata) that are abstracted through reinforcement learning. We demonstrate the user interface (UI) helping the user set intent specifications to the architecture and inspect the difference in allowed and blocked actions.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 4 of its 16 references that are in the map. First posted on arXiv 11 Mar 2021.

Builds on

4 of 16 references.

References · 4

  1. 2017Safe Reinforcement Learning via Shieldingcited by 427
  2. 2020Point-Based Methods for Model Checking in Partially Observable Markov Decision Processescited by 8
  3. 2020Off-policy Learning for Remote Electrical Tilt Optimizationcited by 6
  4. 2017On the Timed Temporal Logic Planning of Coupled Multi-Agent Systemscited by 2

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Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 1.

4 papers.

  1. 2021Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shieldingcited by 9
  2. 2023Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Surveycited by 5
  3. 2022Model Based Residual Policy Learning with Applications to Antenna Controlcited by 1
  4. 2021Safe RAN control: A Symbolic Reinforcement Learning Approachcited by 0