# Reinforcement Learning with Efficient Active Feature Acquisition

arXiv: https://arxiv.org/abs/2011.00825 · HTML: https://mlsanity.com/paper/2011.00825/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 21 of 21)

- [The Essential Role of Causality in Foundation World Models for Embodied AI](https://mlsanity.com/paper/2402.06665/) (2024)
- [Deep Reinforcement Learning for Cost-Effective Medical Diagnosis](https://mlsanity.com/paper/2302.10261/) (2023)
- [A Survey on Active Feature Acquisition Strategies](https://mlsanity.com/paper/2502.11067/) (2025)
- [Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task](https://mlsanity.com/paper/2211.05039/) (2022)
- [Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring](https://mlsanity.com/paper/2303.08271/) (2023)
- [Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings](https://mlsanity.com/paper/2312.01530/) (2023)
- [Stochastic Encodings for Active Feature Acquisition](https://mlsanity.com/paper/2508.01957/) (2025)
- [Evaluation of Active Feature Acquisition Methods for Static Feature Settings](https://mlsanity.com/paper/2312.03619/) (2023)
- [Learning Computational Efficient Bots with Costly Features](https://mlsanity.com/paper/2308.09629/) (2023)
- [Towards Cost Sensitive Decision Making](https://mlsanity.com/paper/2410.03892/) (2024)
- [OCMDP: Observation-Constrained Markov Decision Process](https://mlsanity.com/paper/2411.07087/) (2024)
- [To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning](https://mlsanity.com/paper/2501.12823/) (2025)
- [PRECISE-AS: Personalized Reinforcement Learning for Efficient Point-of-Care Echocardiography in Aortic Stenosis Diagnosis](https://mlsanity.com/paper/2509.02898/) (2025)
- [NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data](https://mlsanity.com/paper/2507.12412/) (2025)
- [Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models](https://mlsanity.com/paper/2605.01662/) (2026)
- [Optimizing Gene-Based Testing for Antibiotic Resistance Prediction](https://mlsanity.com/paper/2502.14919/) (2025)
- [Optimizing Point-of-Care Ultrasound Video Acquisition for Probabilistic Multi-Task Heart Failure Detection](https://mlsanity.com/paper/2602.13658/) (2026)
- [Relaxed Efficient Acquisition of Context and Temporal Features](https://mlsanity.com/paper/2603.11370/) (2026)
- [Sampling-guided exploration of active feature selection policies](https://mlsanity.com/paper/2603.15110/) (2026)
- [Multi-Task Active Learning with Efficient Resource Allocation](https://mlsanity.com/paper/2610.07045/) (2026)
- [Marformer: A Transformer for Predicting Missing Data Distributions](https://mlsanity.com/paper/2610.12379/) (2026)

## Abstract (arXiv)

Solving real-life sequential decision making problems under partial observability involves an exploration-exploitation problem. To be successful, an agent needs to efficiently gather valuable information about the state of the world for making rewarding decisions. However, in real-life, acquiring valuable information is often highly costly, e.g., in the medical domain, information acquisition might correspond to performing a medical test on a patient. This poses a significant challenge for the agent to perform optimally for the task while reducing the cost for information acquisition. In this paper, we propose a model-based reinforcement learning framework that learns an active feature acquisition policy to solve the exploration-exploitation problem during its execution. Key to the success is a novel sequential variational auto-encoder that learns high-quality representations from partially observed states, which are then used by the policy to maximize the task reward in a cost efficient manner. We demonstrate the efficacy of our proposed framework in a control domain as well as using a medical simulator. In both tasks, our proposed method outperforms conventional baselines and results in policies with greater cost efficiency.