# A General Family of Robust Stochastic Operators for Reinforcement Learning

arXiv: https://arxiv.org/abs/1805.08122 · HTML: https://mlsanity.com/paper/1805.08122/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 3 of 3)

- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Topological Foundations of Reinforcement Learning](https://mlsanity.com/paper/2410.03706/) (2024)
- [Bellman operator convergence enhancements in reinforcement learning algorithms](https://mlsanity.com/paper/2505.14564/) (2025)

## Abstract (arXiv)

We consider a new family of operators for reinforcement learning with the goal of alleviating the negative effects and becoming more robust to approximation or estimation errors. Various theoretical results are established, which include showing on a sample path basis that our family of operators preserve optimality and increase the action gap. Our empirical results illustrate the strong benefits of our family of operators, significantly outperforming the classical Bellman operator and recently proposed operators.