# Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization

arXiv: https://arxiv.org/abs/2603.04768 · HTML: https://mlsanity.com/paper/2603.04768/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 56 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Offline RL Without Off-Policy Evaluation](https://mlsanity.com/paper/2106.08909/) (2021)
- [GPflowOpt: A Bayesian Optimization Library using TensorFlow](https://mlsanity.com/paper/1711.03845/) (2017)
- [Towards Robust Neural Networks with Lipschitz Continuity](https://mlsanity.com/paper/1811.09008/) (2018)
- [One-Step Distributional Reinforcement Learning](https://mlsanity.com/paper/2304.14421/) (2023)
- [Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments](https://mlsanity.com/paper/2111.14515/) (2021)
- [DQNAS: Neural Architecture Search using Reinforcement Learning](https://mlsanity.com/paper/2301.06687/) (2023)
- [Learning Physical-Layer Communication with Quantized Feedback](https://mlsanity.com/paper/1904.09252/) (2019)
- [Deep Reinforcement Learning-Based DRAM Equalizer Parameter Optimization Using Latent Representations](https://mlsanity.com/paper/2507.02365/) (2025)

## Abstract (arXiv)

Equalizer parameter optimization is critical for signal integrity in high-speed memory systems operating at multi-gigabit data rates. However, existing methods suffer from computationally expensive eye diagram evaluation, optimization of expected rather than worst-case performance, and absence of uncertainty quantification for deployment decisions. In this paper, we propose a distributional risk-sensitive reinforcement learning framework integrating Information Bottleneck latent representations with Conditional Value-at-Risk optimization. We introduce rate-distortion optimal signal compression achieving 51 times speedup over eye diagrams while quantifying epistemic uncertainty through Monte Carlo dropout. Distributional reinforcement learning with quantile regression enables explicit worst-case optimization, while PAC-Bayesian regularization certifies generalization bounds. Experimental validation on 2.4 million waveforms from eight memory units demonstrated mean improvements of 37.1% and 41.5% for 4-tap and 8-tap equalizer configurations with worst-case guarantees of 33.8% and 38.2%, representing 80.7% and 89.1% improvements over Q-learning baselines. The framework achieved 62.5% high-reliability classification eliminating manual validation for most configurations. These results suggest the proposed framework provides a practical solution for production-scale equalizer optimization with certified worst-case guarantees.