# Satisficing in Time-Sensitive Bandit Learning

arXiv: https://arxiv.org/abs/1803.02855 · HTML: https://mlsanity.com/paper/1803.02855/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2018.

## Built on by (most cited first, 30 of 39)

- [Reinforcement Learning, Bit by Bit](https://mlsanity.com/paper/2103.04047/) (2021)
- [Regret Bounds for Information-Directed Reinforcement Learning](https://mlsanity.com/paper/2206.04640/) (2022)
- [Deciding What to Learn: A Rate-Distortion Approach](https://mlsanity.com/paper/2101.06197/) (2021)
- [Non-Stationary Bandit Learning via Predictive Sampling](https://mlsanity.com/paper/2205.01970/) (2022)
- [Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning](https://mlsanity.com/paper/2206.02072/) (2022)
- [The Value of Information When Deciding What to Learn](https://mlsanity.com/paper/2110.13973/) (2021)
- [Pareto Optimal Model Selection in Linear Bandits](https://mlsanity.com/paper/2102.06593/) (2021)
- [Lenient Regret and Good-Action Identification in Gaussian Process Bandits](https://mlsanity.com/paper/2102.05793/) (2021)
- [An Information-Theoretic Analysis of Nonstationary Bandit Learning](https://mlsanity.com/paper/2302.04452/) (2023)
- [Be Greedy in Multi-Armed Bandits](https://mlsanity.com/paper/2101.01086/) (2021)
- [On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning](https://mlsanity.com/paper/2210.16877/) (2022)
- [A Bit Better? Quantifying Information for Bandit Learning](https://mlsanity.com/paper/2102.09488/) (2021)
- [Gaussian Imagination in Bandit Learning](https://mlsanity.com/paper/2201.01902/) (2022)
- [Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning](https://mlsanity.com/paper/2209.07676/) (2022)
- [Bayesian Reinforcement Learning with Limited Cognitive Load](https://mlsanity.com/paper/2305.03263/) (2023)
- [Information Capacity Regret Bounds for Bandits with Mediator Feedback](https://mlsanity.com/paper/2402.10282/) (2024)
- [Satisficing Exploration for Deep Reinforcement Learning](https://mlsanity.com/paper/2407.12185/) (2024)
- [Robust Bayesian Satisficing](https://mlsanity.com/paper/2308.08291/) (2023)
- [Domain Generalization without Excess Empirical Risk](https://mlsanity.com/paper/2308.15856/) (2023)
- [On Bits and Bandits: Quantifying the Regret-Information Trade-off](https://mlsanity.com/paper/2405.16581/) (2024)
- [Adaptive Instruction Composition for Automated LLM Red-Teaming](https://mlsanity.com/paper/2604.21159/) (2026)
- [Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2206.02025/) (2022)
- [Policy Testing in Markov Decision Processes](https://mlsanity.com/paper/2505.15342/) (2025)
- [Online Mixture of Experts: No-Regret Learning for Optimal Collective Decision-Making](https://mlsanity.com/paper/2510.21788/) (2025)
- [Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits](https://mlsanity.com/paper/2408.15535/) (2024)
- [Robust Satisficing Gaussian Process Bandits Under Adversarial Attacks](https://mlsanity.com/paper/2506.01625/) (2025)
- [Not all uncertainty is alike: volatility, stochasticity, and exploration](https://mlsanity.com/paper/2605.19215/) (2026)
- [Thompson Sampling with Virtual Helping Agents](https://mlsanity.com/paper/2209.08197/) (2022)
- [Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar](https://mlsanity.com/paper/2212.00615/) (2022)
- [Machine learning at the mesoscale: a computation-dissipation bottleneck](https://mlsanity.com/paper/2307.02379/) (2023)

## Abstract (arXiv)

Much of the recent literature on bandit learning focuses on algorithms that aim to converge on an optimal action. One shortcoming is that this orientation does not account for time sensitivity, which can play a crucial role when learning an optimal action requires much more information than near-optimal ones. Indeed, popular approaches such as upper-confidence-bound methods and Thompson sampling can fare poorly in such situations. We consider instead learning a satisficing action, which is near-optimal while requiring less information, and propose satisficing Thompson sampling, an algorithm that serves this purpose. We establish a general bound on expected discounted regret and study the application of satisficing Thompson sampling to linear and infinite-armed bandits, demonstrating arbitrarily large benefits over Thompson sampling. We also discuss the relation between the notion of satisficing and the theory of rate distortion, which offers guidance on the selection of satisficing actions.