# On the Reduction of Variance and Overestimation of Deep Q-Learning

arXiv: https://arxiv.org/abs/1910.05983 · HTML: https://mlsanity.com/paper/1910.05983/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Efficient Continuous Control with Double Actors and Regularized Critics](https://mlsanity.com/paper/2106.03050/) (2021)
- [Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse](https://mlsanity.com/paper/2305.18443/) (2023)
- [Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks](https://mlsanity.com/paper/2210.03325/) (2022)
- [Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes](https://mlsanity.com/paper/2302.10434/) (2023)

## Abstract (arXiv)

The breakthrough of deep Q-Learning on different types of environments revolutionized the algorithmic design of Reinforcement Learning to introduce more stable and robust algorithms, to that end many extensions to deep Q-Learning algorithm have been proposed to reduce the variance of the target values and the overestimation phenomena. In this paper, we examine new methodology to solve these issues, we propose using Dropout techniques on deep Q-Learning algorithm as a way to reduce variance and overestimation. We also present experiments conducted on benchmark environments, demonstrating the effectiveness of our methodology in enhancing stability and reducing both variance and overestimation in model performance.