# Conservative Exploration using Interleaving

arXiv: https://arxiv.org/abs/1806.00892 · HTML: https://mlsanity.com/paper/1806.00892/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2018.

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

- [A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning](https://mlsanity.com/paper/2106.11692/) (2021)
- [Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints](https://mlsanity.com/paper/2401.11563/) (2024)
- [Anytime-Competitive Reinforcement Learning with Policy Prior](https://mlsanity.com/paper/2311.01568/) (2023)
- [Conservative Exploration for Policy Optimization via Off-Policy Policy Evaluation](https://mlsanity.com/paper/2312.15458/) (2023)

## Abstract (arXiv)

In many practical problems, a learning agent may want to learn the best action in hindsight without ever taking a bad action, which is significantly worse than the default production action. In general, this is impossible because the agent has to explore unknown actions, some of which can be bad, to learn better actions. However, when the actions are combinatorial, this may be possible if the unknown action can be evaluated by interleaving it with the production action. We formalize this concept as learning in stochastic combinatorial semi-bandits with exchangeable actions. We design efficient learning algorithms for this problem, bound their n-step regret, and evaluate them on both synthetic and real-world problems. Our real-world experiments show that our algorithms can learn to recommend K most attractive movies without ever violating a strict production constraint, both overall and subject to a diversity constraint.