# ACCoRD: Actor-Critic Conflict Resolution with Deep learning for O-RAN xApps

arXiv: https://arxiv.org/abs/2605.22306 · HTML: https://mlsanity.com/paper/2605.22306/

## 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 6 of its 11 references that are in the map.
- First posted on arXiv 21 May 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, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [PACIFISTA: Conflict Evaluation and Management in Open RAN](https://mlsanity.com/paper/2405.04395/) (2024)
- [Conflict Mitigation Framework and Conflict Detection in O-RAN Near-RT RIC](https://mlsanity.com/paper/2305.07117/) (2023)
- [COMIX: Generalized Conflict Management in O-RAN xApps -- Architecture, Workflow, and a Power Control case](https://mlsanity.com/paper/2501.14619/) (2025)
- [xApp Distillation: AI-based Conflict Mitigation in B5G O-RAN](https://mlsanity.com/paper/2407.03068/) (2024)
- [QACM: QoS-Aware xApp Conflict Mitigation in Open RAN](https://mlsanity.com/paper/2405.07324/) (2024)

## Abstract (arXiv)

Conflict Mitigation (ConMit) is a crucial part of intelligent network control in Open Radio Access Networks (O-RAN). In this paper, we propose a method named ACCoRD to resolve detected control conflicts in Near-Real Time RAN Intelligent Controller using a Conflict Resolution (CR) Agent with an Artificial Neural Network (ANN) trained with a reinforcement learning algorithm PPO-Clip. The implemented ANN analyzes data about the network and conflicting control decisions to infer optimal CR actions. The CR Agent gathers feedback from the network after each resolved conflict to assess its efficiency and adjust the ANN's weights during batch training. The evaluation of the proposed approach is based on simulation data. A new methodology for evaluating CR solutions is proposed. Results show that the proposed ANN-based method improves on the efficiency of rule-based approaches by significantly reducing negative network events caused by conflicting control decisions in medium and high traffic scenarios.