# DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN

arXiv: https://arxiv.org/abs/2208.00383 · HTML: https://mlsanity.com/paper/2208.00383/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [CFR-RL: Traffic Engineering with Reinforcement Learning in SDN](https://mlsanity.com/paper/2004.11986/) (2020)

## Built on by (most cited first, 6 of 6)

- [DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN](https://mlsanity.com/paper/2305.19077/) (2023)
- [Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN](https://mlsanity.com/paper/2305.10440/) (2023)
- [An Intelligent SDWN Routing Algorithm Based on Network Situational Awareness and Deep Reinforcement Learning](https://mlsanity.com/paper/2305.10441/) (2023)
- [MA-CDMR: An Intelligent Cross-domain Multicast Routing Method based on Multiagent Deep Reinforcement Learning in Multi-domain SDWN](https://mlsanity.com/paper/2409.05888/) (2024)
- [Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic](https://mlsanity.com/paper/2507.22174/) (2025)
- [Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks](https://mlsanity.com/paper/2510.11109/) (2025)

## Abstract (arXiv)

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment.