# A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation

arXiv: https://arxiv.org/abs/2304.12660 · HTML: https://mlsanity.com/paper/2304.12660/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 25 Apr 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Learning Optimal Resource Allocations in Wireless Systems](https://mlsanity.com/paper/1807.08088/) (2018)
- [Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication](https://mlsanity.com/paper/1911.03264/) (2019)
- [Autonomous Braking System via Deep Reinforcement Learning](https://mlsanity.com/paper/1702.02302/) (2017)
- [Performance Requirements of Advanced Healthcare Services over Future Cellular Systems](https://mlsanity.com/paper/1906.01503/) (2019)
- [Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients](https://mlsanity.com/paper/2304.09488/) (2023)
- [Robust Deep Reinforcement Learning Scheduling via Weight Anchoring](https://mlsanity.com/paper/2304.10176/) (2023)

## Built on by (most cited first, 1 of 1)

- [HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning](https://mlsanity.com/paper/2406.03997/) (2024)

## Abstract (arXiv)

With increasing complexity of modern communication systems, machine learning algorithms have become a focal point of research. However, performance demands have tightened in parallel to complexity. For some of the key applications targeted by future wireless, such as the medical field, strict and reliable performance guarantees are essential, but vanilla machine learning methods have been shown to struggle with these types of requirements. Therefore, the question is raised whether these methods can be extended to better deal with the demands imposed by such applications. In this paper, we look at a combinatorial resource allocation challenge with rare, significant events which must be handled properly. We propose to treat this as a multi-task learning problem, select two methods from this domain, Elastic Weight Consolidation and Gradient Episodic Memory, and integrate them into a vanilla actor-critic scheduler. We compare their performance in dealing with Black Swan Events with the state-of-the-art of augmenting the training data distribution and report that the multi-task approach proves highly effective.