# On the Comparison of Reinforcement Learning and Adaptive Control for Linear Systems under Packet Loss and Uncertainty

arXiv: https://arxiv.org/abs/2606.32003 · HTML: https://mlsanity.com/paper/2606.32003/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 10 of 10)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Stability Analysis using Quadratic Constraints for Systems with Neural Network Controllers](https://mlsanity.com/paper/2006.07579/) (2020)
- [Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown Environment](https://mlsanity.com/paper/2007.11808/) (2020)
- [Lyapunov Criterion for Stochastic Systems and Its Applications in Distributed Computation](https://mlsanity.com/paper/1902.04332/) (2019)
- [Adaptive Covariance Kalman Filtering and Nonlinear Decoupling Control via Feedback Linearization for a Three-Tank Process](https://mlsanity.com/paper/2304.04144/) (2023)
- [Distributed Estimation with Decentralized Control for Quadruple-Tank Process](https://mlsanity.com/paper/2304.04763/) (2023)
- [Distributed Adaptive Control of Disturbed Interconnected Systems with High-Order Tuners](https://mlsanity.com/paper/2405.15178/) (2024)
- [Model Reference Adaptive Control of Networked Systems with State and Input Delays](https://mlsanity.com/paper/2506.19138/) (2025)

## Abstract (arXiv)

This paper presents a comparative study between Adaptive Quantized Control (AQC) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning for uncertain linear systems with input quantization over communication channels subject to packet loss. The considered setting also includes dynamic switching from a nominal unstable system to a more unstable one during operation. The AQC is designed for unknown system dynamics using acknowledgment messages to compensate for packet losses, whereas the DDPG controller is trained using the nominal system model without acknowledgment messages. Numerical results show that the DDPG controller achieves faster transient responses and improved damping within its training environment. However, under model uncertainty, packet loss, and dynamic switching, the AQC consistently demonstrates superior robustness owing to its rigorous Lyapunov stability guarantees. These results highlight the trade-off between data-driven performance and model-based robustness, and provide insight into the applicability of reinforcement learning and adaptive control for networked uncertain systems.