# Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks

arXiv: https://arxiv.org/abs/2502.01129 · HTML: https://mlsanity.com/paper/2502.01129/

## 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 3 of its 7 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## 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, 3 of 3)

- [Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2002.06215/) (2020)
- [Implications of Decentralized Q-learning Resource Allocation in Wireless Networks](https://mlsanity.com/paper/1705.10508/) (2017)
- [Context-Aware Resource Allocation in Cellular Networks](https://mlsanity.com/paper/1406.1910/) (2014)

## Abstract (arXiv)

This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, and user equipment is created. Using the RLlib library, various DRL algorithms such as Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) are then applied. These algorithms are compared based on their ability to optimize resource allocation, focusing on the impact of different learning rates and scheduling policies. The findings demonstrate that the choice of algorithm and learning rate significantly influences system performance, with DRL providing more efficient resource allocation compared to traditional methods.