Stochastic Multi-Objective Multi-Armed Bandits: Regret Definition and Algorithm

• URL: http://arxiv.org/abs/2506.13125v1
• Date: Mon, 16 Jun 2025 06:09:28 GMT
• Title: Stochastic Multi-Objective Multi-Armed Bandits: Regret Definition and Algorithm
• Authors: Mansoor Davoodi, Setareh Maghsudi,
• Abstract summary: Multi-objective multi-armed bandits (MO-MAB) problems are widely applied to online optimization tasks.<n>We propose a novel and comprehensive regret metric that ensures balanced performance across conflicting objectives.
• Score: 6.046591474843391
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multi-armed bandit (MAB) problems are widely applied to online optimization tasks that require balancing exploration and exploitation. In practical scenarios, these tasks often involve multiple conflicting objectives, giving rise to multi-objective multi-armed bandits (MO-MAB). Existing MO-MAB approaches predominantly rely on the Pareto regret metric introduced in \cite{drugan2013designing}. However, this metric has notable limitations, particularly in accounting for all Pareto-optimal arms simultaneously. To address these challenges, we propose a novel and comprehensive regret metric that ensures balanced performance across conflicting objectives. Additionally, we introduce the concept of \textit{Efficient Pareto-Optimal} arms, which are specifically designed for online optimization. Based on our new metric, we develop a two-phase MO-MAB algorithm that achieves sublinear regret for both Pareto-optimal and efficient Pareto-optimal arms.

Related papers

• OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL [63.388513841293616]
  Existing forgery detection methods fail to handle the interleaved text, images, and videos prevalent in real-world misinformation.<n>To bridge this gap, this paper targets to develop a unified framework for omnibus vision-language forgery detection and grounding.<n>We propose textbf OmniVL-Guard, a balanced reinforcement learning framework for omnibus vision-language forgery detection and grounding.
  arXiv  Detail & Related papers  (2026-02-11T09:41:36Z)
• Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning [56.240199425429445]
  Multi-Robot Motion Planning (MPMP) involves generating trajectories for multiple robots operating in a shared continuous workspace.<n>While discrete multi-agent finding (MAPF) methods are broadly adopted due to their scalability, their coarse discretization trajectory quality.<n>This paper tackles limitations of two approaches by introducing discrete MAPF solvers with constrained generative diffusion models.
  arXiv  Detail & Related papers  (2025-08-27T17:59:36Z)
• Reinforcement learning with combinatorial actions for coupled restless bandits [62.89013331120493]
  We propose SEQUOIA, an RL algorithm that directly optimize for long-term reward over the feasible action space.<n>We empirically validate SEQUOIA on four novel restless bandit problems with constraints: multiple interventions, path constraints, bipartite matching, and capacity constraints.
  arXiv  Detail & Related papers  (2025-03-01T21:25:21Z)
• Semi-Parametric Batched Global Multi-Armed Bandits with Covariates [0.48342038441006807]
  The multi-armed bandits (MAB) framework is a widely used approach for sequential decision-making.<n>We propose a novel semi-parametric framework for batched bandits with coparametrics and a shared parameter across arms.<n>Our algorithm, Batched single-Index Dynamic binning and Successive arm elimination (BIDS), employs a batched successive arm elimination strategy.
  arXiv  Detail & Related papers  (2025-03-01T17:23:55Z)
• Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models [57.45019514036948]
  Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics.<n>This work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces.
  arXiv  Detail & Related papers  (2024-12-23T21:27:19Z)
• Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond [58.39457881271146]
  We introduce a novel framework of multi-armed bandits (CMAB) with multivariant and probabilistically triggering arms (CMAB-MT)<n>Compared with existing CMAB works, CMAB-MT not only enhances the modeling power but also allows improved results by leveraging distinct statistical properties for multivariant random variables.<n>Our framework can include many important problems as applications, such as episodic reinforcement learning (RL) and probabilistic maximum coverage for goods distribution.
  arXiv  Detail & Related papers  (2024-06-03T14:48:53Z)
• Meta-Learning Adversarial Bandit Algorithms [55.72892209124227]
  We study online meta-learning with bandit feedback. We learn to tune online mirror descent generalization (OMD) with self-concordant barrier regularizers.
  arXiv  Detail & Related papers  (2023-07-05T13:52:10Z)
• Pareto Regret Analyses in Multi-objective Multi-armed Bandit [22.17126026244685]
  We study optimality in multi-objective multi-armed bandit. We present new algorithms assuming both with and without prior information of the multi-objective multi-armed bandit setting. The algorithms are shown optimal in adversarial settings and nearly optimal up to a logarithmic factor in settings simultaneously.
  arXiv  Detail & Related papers  (2022-12-01T21:44:27Z)
• Meta-Learning Adversarial Bandits [49.094361442409785]
  We study online learning with bandit feedback across multiple tasks, with the goal of improving average performance across tasks if they are similar according to some natural task-similarity measure. As the first to target the adversarial setting, we design a meta-algorithm that setting-specific guarantees for two important cases: multi-armed bandits (MAB) and bandit optimization (BLO) Our guarantees rely on proving that unregularized follow-the-leader combined with multiplicative weights is enough to online learn a non-smooth and non-B sequence.
  arXiv  Detail & Related papers  (2022-05-27T17:40:32Z)
• DORB: Dynamically Optimizing Multiple Rewards with Bandits [101.68525259222164]
  Policy-based reinforcement learning has proven to be a promising approach for optimizing non-differentiable evaluation metrics for language generation tasks. We use the Exp3 algorithm for bandits and formulate two approaches for bandit rewards: (1) Single Multi-reward Bandit (SM-Bandit); (2) Hierarchical Multi-reward Bandit (HM-Bandit) We empirically show the effectiveness of our approaches via various automatic metrics and human evaluation on two important NLG tasks.
  arXiv  Detail & Related papers  (2020-11-15T21:57:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.