# DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback

arXiv: https://arxiv.org/abs/2410.05527 · HTML: https://mlsanity.com/paper/2410.05527/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reward modeling and RLHF, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 38 of its 66 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Reward modeling and RLHF](https://mlsanity.com/alignment/reward-modeling-and-rlhf/)

## Builds on (most cited first, 30 of 38)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [A General Theoretical Paradigm to Understand Learning from Human Preferences](https://mlsanity.com/paper/2310.12036/) (2023)
- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [Provably Efficient Reinforcement Learning with Linear Function Approximation](https://mlsanity.com/paper/1907.05388/) (2019)
- [SLiC-HF: Sequence Likelihood Calibration with Human Feedback](https://mlsanity.com/paper/2305.10425/) (2023)
- [Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint](https://mlsanity.com/paper/2312.11456/) (2023)
- [Reward learning from human preferences and demonstrations in Atari](https://mlsanity.com/paper/1811.06521/) (2018)
- [Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons](https://mlsanity.com/paper/2301.11270/) (2023)
- [Generalized Preference Optimization: A Unified Approach to Offline Alignment](https://mlsanity.com/paper/2402.05749/) (2024)
- [Dueling RL: Reinforcement Learning with Trajectory Preferences](https://mlsanity.com/paper/2111.04850/) (2021)
- [Contextual Dueling Bandits](https://mlsanity.com/paper/1502.06362/) (2015)
- [Is RLHF More Difficult than Standard RL?](https://mlsanity.com/paper/2306.14111/) (2023)
- [Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation](https://mlsanity.com/paper/2205.11140/) (2022)
- [Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem](https://mlsanity.com/paper/1506.02550/) (2015)
- [Collapsing Bandits and Their Application to Public Health Interventions](https://mlsanity.com/paper/2007.04432/) (2020)
- [Whittle index based Q-learning for restless bandits with average reward](https://mlsanity.com/paper/2004.14427/) (2020)
- [Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability](https://mlsanity.com/paper/2111.12306/) (2021)
- [Double Thompson Sampling for Dueling Bandits](https://mlsanity.com/paper/1604.07101/) (2016)
- [Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare](https://mlsanity.com/paper/2105.07965/) (2021)
- [Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems](https://mlsanity.com/paper/1905.12673/) (2019)
- [Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits](https://mlsanity.com/paper/2011.02664/) (2020)
- [NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL](https://mlsanity.com/paper/2110.02128/) (2021)
- [Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization](https://mlsanity.com/paper/2402.10342/) (2024)
- [Optimistic Whittle Index Policy: Online Learning for Restless Bandits](https://mlsanity.com/paper/2205.15372/) (2022)
- [Restless Bandits with Many Arms: Beating the Central Limit Theorem](https://mlsanity.com/paper/2107.11911/) (2021)
- [Deadline Scheduling as Restless Bandits](https://mlsanity.com/paper/1610.00399/) (2016)
- [Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation](https://mlsanity.com/paper/2310.02147/) (2023)

## Built on by (most cited first, 2 of 2)

- [Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact](https://mlsanity.com/paper/2412.07880/) (2024)
- [Online Convex Optimization with Dueling Feedback](https://mlsanity.com/paper/2608.15050/) (2026)

## Abstract (arXiv)

Restless multi-armed bandits (RMAB) has been widely used to model constrained sequential decision making problems, where the state of each restless arm evolves according to a Markov chain and each state transition generates a scalar reward. However, the success of RMAB crucially relies on the availability and quality of reward signals. Unfortunately, specifying an exact reward function in practice can be challenging and even infeasible. In this paper, we introduce Pref-RMAB, a new RMAB model in the presence of textitpreference signals, where the decision maker only observes pairwise preference feedback rather than scalar reward from the activated arms at each decision epoch. Preference feedback, however, arguably contains less information than the scalar reward, which makes Pref-RMAB seemingly more difficult. To address this challenge, we present a direct online preference learning (DOPL) algorithm for Pref-RMAB to efficiently explore the unknown environments, adaptively collect preference data in an online manner, and directly leverage the preference feedback for decision-makings. We prove that DOPL yields a sublinear regret. To our best knowledge, this is the first algorithm to ensure tildeO(√Tln T) regret for RMAB with preference feedback. Experimental results further demonstrate the effectiveness of DOPL.