Preference-based Multi-Objective Reinforcement Learning

• URL: http://arxiv.org/abs/2507.14066v1
• Date: Fri, 18 Jul 2025 16:43:04 GMT
• Title: Preference-based Multi-Objective Reinforcement Learning
• Authors: Ni Mu, Yao Luan, Qing-Shan Jia,
• Abstract summary: This paper introduces preference-based MORL (Pb-MORL), which formalizes the integration of preferences into the MORL framework.<n>To guide policy optimization using preferences, our method constructs a multi-objective reward model that aligns with the given preferences.<n>Experiments in benchmark multi-objective tasks, a multi-energy management task, and an autonomous driving task on a multi-line highway show that our method performs competitively.
• Score: 5.031225669460861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-objective reinforcement learning (MORL) is a structured approach for optimizing tasks with multiple objectives. However, it often relies on pre-defined reward functions, which can be hard to design for balancing conflicting goals and may lead to oversimplification. Preferences can serve as more flexible and intuitive decision-making guidance, eliminating the need for complicated reward design. This paper introduces preference-based MORL (Pb-MORL), which formalizes the integration of preferences into the MORL framework. We theoretically prove that preferences can derive policies across the entire Pareto frontier. To guide policy optimization using preferences, our method constructs a multi-objective reward model that aligns with the given preferences. We further provide theoretical proof to show that optimizing this reward model is equivalent to training the Pareto optimal policy. Extensive experiments in benchmark multi-objective tasks, a multi-energy management task, and an autonomous driving task on a multi-line highway show that our method performs competitively, surpassing the oracle method, which uses the ground truth reward function. This highlights its potential for practical applications in complex real-world systems.

Related papers

• Interpretability by Design for Efficient Multi-Objective Reinforcement Learning [0.5524804393257919]
  Multi-objective reinforcement learning (MORL) aims at optimising several, often conflicting goals in order to improve flexibility and reliability of RL in practical tasks.<n>This can be achieved by finding diverse policies that are optimal for some objective preferences and non-dominated by optimal policies for other preferences so that they form a Pareto front in the multi-objective performance space.
  arXiv  Detail & Related papers  (2025-06-04T14:52:18Z)
• MOSLIM:Align with diverse preferences in prompts through reward classification [6.6431471703308915]
  We introduce a novel multi-objective alignment method, MOSLIM, which utilizes a single reward model and policy model to address diverse objectives.<n> MOSLIM provides a flexible way to control these objectives through prompting and does not require preference training during SFT phase.<n>We demonstrate the efficacy of our proposed method across several multi-objective benchmarks and conduct ablation studies on various reward model sizes and policy optimization methods.
  arXiv  Detail & Related papers  (2025-05-24T12:22:21Z)
• Collab: Controlled Decoding using Mixture of Agents for LLM Alignment [90.6117569025754]
  Reinforcement learning from human feedback has emerged as an effective technique to align Large Language models.<n>Controlled Decoding provides a mechanism for aligning a model at inference time without retraining.<n>We propose a mixture of agent-based decoding strategies leveraging the existing off-the-shelf aligned LLM policies.
  arXiv  Detail & Related papers  (2025-03-27T17:34:25Z)
• Preference-Guided Diffusion for Multi-Objective Offline Optimization [64.08326521234228]
  We propose a preference-guided diffusion model for offline multi-objective optimization.<n>Our guidance is a preference model trained to predict the probability that one design dominates another.<n>Our results highlight the effectiveness of classifier-guided diffusion models in generating diverse and high-quality solutions.
  arXiv  Detail & Related papers  (2025-03-21T16:49:38Z)
• Robust Multi-Objective Preference Alignment with Online DPO [6.434799451791957]
  Multi-objective preference alignment is critical for developing AI systems that are personalizable, helpful, and safe.<n>Existing approaches are either computationally expensive to train or do not sufficiently steer model behaviors.<n>This paper introduces the Multi-Objective Online DPO algorithm, designed to robustly and efficiently align model behaviors with multiple, potentially conflicting human preferences.
  arXiv  Detail & Related papers  (2025-03-01T02:01:49Z)
• Self-Improvement Towards Pareto Optimality: Mitigating Preference Conflicts in Multi-Objective Alignment [74.25832963097658]
  Multi-Objective Alignment (MOA) aims to align responses with multiple human preference objectives.<n>We find that DPO-based MOA approaches suffer from widespread preference conflicts in the data.
  arXiv  Detail & Related papers  (2025-02-20T08:27:00Z)
• Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion [53.33473557562837]
  Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost. We propose a practical and scalable approach to solve this problem via mixture of experts (MoE) based model fusion. By ensembling the weights of specialized single-task models, the MoE module can effectively capture the trade-offs between multiple objectives.
  arXiv  Detail & Related papers  (2024-06-14T07:16:18Z)
• MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization [45.410121761165634]
  RL-based techniques can be employed to search for prompts that, when fed into a target language model, maximize a set of user-specified reward functions. Current techniques focus on maximizing the average of reward functions, which does not necessarily lead to prompts that achieve balance across rewards.
  arXiv  Detail & Related papers  (2024-02-18T21:25:09Z)
• Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment [46.44464839353993]
  We introduce Rewards-in-Context (RiC), which conditions the response of a foundation model on multiple rewards in its prompt context. RiC only requires supervised fine-tuning of a single foundation model and supports dynamic adjustment for user preferences during inference time.
  arXiv  Detail & Related papers  (2024-02-15T18:58:31Z)
• Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization [76.09576643028362]
  We present Multi-Objective Direct Preference Optimization (MODPO) for multiple alignment objectives. MODPO folds language modeling directly into reward modeling, training language models as implicit collective reward models. It theoretically yields the same optimal solutions as MORLHF but is practically more stable and efficient.
  arXiv  Detail & Related papers  (2023-10-05T17:35:26Z)
• Reparameterized Policy Learning for Multimodal Trajectory Optimization [61.13228961771765]
  We investigate the challenge of parametrizing policies for reinforcement learning in high-dimensional continuous action spaces. We propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories. We present a practical model-based RL method, which leverages the multimodal policy parameterization and learned world model.
  arXiv  Detail & Related papers  (2023-07-20T09:05:46Z)
• PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm [0.18416014644193063]
  We propose a novel MORL algorithm that trains a single universal network to cover the entire preference space scalable to continuous robotic tasks. PD-MORL achieves up to 25% larger hypervolume for challenging continuous control tasks and uses an order of magnitude fewer trainable parameters compared to prior approaches.
  arXiv  Detail & Related papers  (2022-08-16T19:23:02Z)
• Provable Multi-Objective Reinforcement Learning with Generative Models [98.19879408649848]
  We study the problem of single policy MORL, which learns an optimal policy given the preference of objectives. Existing methods require strong assumptions such as exact knowledge of the multi-objective decision process. We propose a new algorithm called model-based envelop value (EVI) which generalizes the enveloped multi-objective $Q$-learning algorithm.
  arXiv  Detail & Related papers  (2020-11-19T22:35:31Z)

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.