Multi-Objective Constraint Inference using Inverse reinforcement learning

• URL: http://arxiv.org/abs/2605.06951v1
• Date: Thu, 07 May 2026 21:09:09 GMT
• Title: Multi-Objective Constraint Inference using Inverse reinforcement learning
• Abstract summary: Multi-Objective Constraint Inference (MOCI) is a novel framework designed to jointly extract shared constraints and individual preferences from heterogeneous expert trajectories.<n>MOCI effectively models and learns from diverse, and potentially conflicting, behaviors.
• Score: 4.8029888032686445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Constraint inference is widely considered essential to align reinforcement learning agents with safety boundaries and operational guidelines by observing expert demonstrations. However, existing approaches typically assume homogeneous demonstrations (i.e., generated by a single expert or multiple experts with identical objectives). They also have limited ability to capture individual preferences and often suffer from computational inefficiencies. In this paper, we introduce Multi-Objective Constraint Inference (MOCI), a novel framework designed to jointly extract shared constraints and individual preferences from heterogeneous expert trajectories, where multiple experts pursue different objectives. MOCI effectively models and learns from diverse, and potentially conflicting, behaviors. Empirical evaluations demonstrate that MOCI significantly outperforms existing baselines, achieving improved predictive performance, and maintaining competitive computational efficiency on a standard grid-world benchmark. These results establish MOCI as an accurate, flexible, and computationally practical approach for real-world constraint inference and preference learning tasks.

Related papers

• Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion [24.714655444299435]
  Multi-Objective Reinforcement Learning (MORL) extends standard RL by optimizing policies with respect to multiple, often conflicting, objectives.<n>We propose a MORL framework that integrates the max-min criterion with explicit constraint satisfaction.
  arXiv  Detail & Related papers  (2026-05-29T14:52:12Z)
• Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation [49.86232017439639]
  We introduce Multi-Output Augmented Behavioral Cloning (MA-BC)<n>MA-BC partitions divergent expert data while pooling state-action pairs where no behavior conflict is observed.<n>We establish a novel lower bound for multi-objective imitation learning, demonstrating that MA-BC is minimax optimal.
  arXiv  Detail & Related papers  (2026-05-12T11:49:08Z)
• Prediction-powered Inference by Mixture of Experts [14.293038907072598]
  We introduce an MOE-powered semi-supervised inference framework built upon prediction-powered inference (PPI)<n>Motivated by the variance reduction principle underlying PPI, the proposed framework seeks the mixture of experts that achieves the smallest possible variance.<n>The framework is flexible and applies to mean estimation, linear regression, quantile estimation, and general M-estimation.
  arXiv  Detail & Related papers  (2026-04-30T14:08:17Z)
• TREX: Trajectory Explanations for Multi-Objective Reinforcement Learning [1.2922946578413577]
  Trajectory based Explainability framework to explain Multi-objective Reinforcement Learning policies.<n>Trajectory based Explainability framework to explain Multi-objective Reinforcement Learning policies.
  arXiv  Detail & Related papers  (2026-03-23T13:55:14Z)
• Is Softmax Loss All You Need? A Principled Analysis of Softmax-family Loss [91.61796429377041]
  The Softmax loss is one of the most widely employed surrogate objectives for classification and ranking tasks.<n>We investigate whether different surrogates achieve consistency with classification and ranking metrics, and analyze their gradient dynamics to reveal distinct convergence behaviors.<n>Our results establish a principled foundation and offer practical guidance for loss selections in large-class machine learning applications.
  arXiv  Detail & Related papers  (2026-01-30T09:24:52Z)
• From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation [59.27094165576015]
  We propose a novel learning paradigm (UniMod) that transitions from sparse decision-making to dense reasoning traces.<n>By constructing structured trajectories encompassing evidence grounding, modality assessment, risk mapping, policy decision, and response generation, we reformulate monolithic decision tasks into a multi-dimensional boundary learning process.<n>We introduce specialized optimization strategies to decouple task-specific parameters and rebalance training dynamics, effectively resolving interference between diverse objectives in multi-task learning.
  arXiv  Detail & Related papers  (2026-01-28T09:29:40Z)
• Imitation Learning for Combinatorial Optimisation under Uncertainty [1.0781866671930855]
  This paper introduces a systematic taxonomy of experts for IL optimisation under uncertainty.<n>Experts are classified along three dimensions: (i) their treatment of uncertainty, including myopic, deterministic, full-information, two-stage, and multi-stage formulations; (ii) their level of optimality, distinguishing task-optimal and approximate experts; and (iii) their interaction mode with the learner, ranging from one-shot supervision to iterative, interactive schemes.
  arXiv  Detail & Related papers  (2026-01-08T21:16:25Z)
• Input Domain Aware MoE: Decoupling Routing Decisions from Task Optimization in Mixture of Experts [19.707274733121412]
  Sparse Mixture of Experts (sMoE) has become a pivotal approach for scaling large vision-language models.<n>We propose Input Domain Aware MoE, a novel routing framework that leverages a probabilistic mixture model to better partition the input space.<n>By modeling routing probabilities as a mixture of distributions, our method enables experts to develop clear specialization boundaries while achieving balanced utilization.
  arXiv  Detail & Related papers  (2025-10-18T11:01:03Z)
• Training Matryoshka Mixture-of-Experts for Elastic Inference-Time Expert Utilization [60.309915093470416]
  Matryoshka MoE (M-MoE) is a training framework that instills a coarse-to-fine structure directly into the expert ensemble.<n>Our work paves the way for more practical and adaptable deployments of large-scale MoE models.
  arXiv  Detail & Related papers  (2025-09-30T16:56:44Z)
• One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning [52.966712416640085]
  We propose SMoPE, a novel framework that integrates the benefits of both task-specific and shared prompt strategies.<n>SMoPE consistently outperforms task-specific prompt methods and achieves performance competitive with state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-09-29T08:54:58Z)
• Convergence Rates for Softmax Gating Mixture of Experts [78.3687645289918]
  Mixture of experts (MoE) has emerged as an effective framework to advance the efficiency and scalability of machine learning models.<n>Central to the success of MoE is an adaptive softmax gating mechanism which takes responsibility for determining the relevance of each expert to a given input and then dynamically assigning experts their respective weights.<n>We perform a convergence analysis of parameter estimation and expert estimation under the MoE equipped with the standard softmax gating or its variants, including a dense-to-sparse gating and a hierarchical softmax gating.
  arXiv  Detail & Related papers  (2025-03-05T06:11:24Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)

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.