A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning

• URL: http://arxiv.org/abs/2604.24532v1
• Date: Mon, 27 Apr 2026 14:32:44 GMT
• Title: A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning
• Abstract summary: We propose leveraging reward-free reinforcement learning (RFRL) for multi-objective reinforcement learning (MORL)<n>RFRL has historically been studied independently of MORL, making it a natural fit for MORL's challenge of handling unknown user preferences.<n>We show that our approach significantly outperforms the state-of-the-art MORL methods across diverse MO-Gymnasium tasks.
• Score: 19.061803766467627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many sequential decision-making tasks involve optimizing multiple conflicting objectives, requiring policies that adapt to different user preferences. In multi-objective reinforcement learning (MORL), one widely studied approach} addresses this by training a single policy network conditioned on preference-weighted rewards. In this paper, we explore a novel algorithmic perspective: leveraging reward-free reinforcement learning (RFRL) for MORL. While RFRL has historically been studied independently of MORL, it learns optimal policies for any possible reward function, making it a natural fit for MORL's challenge of handling unknown user preferences. We propose using the RFRL's training objective as an auxiliary task to enhance MORL, enabling more effective knowledge sharing beyond the multi-objective reward function given at training time. To this end, we adapt a state-of-the-art RFRL algorithm to the MORL setting and introduce a preference-guided exploration strategy that focuses learning on relevant parts of the environment. Through extensive experiments and ablation studies, we demonstrate that our approach significantly outperforms the state-of-the-art MORL methods across diverse MO-Gymnasium tasks, achieving superior performance and data efficiency. This work provides the first systematic adaptation of RFRL to MORL, demonstrating its potential as a scalable and empirically effective solution to multi-objective policy learning.

Related papers

• LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback [4.210862582629798]
  We present LEMUR: Learning to Align with Multi-Objective Reinforcement Learning with Preference feedback.<n>It is a novel framework where an agent interactively learns from the preferences of multiple humans to learn optimal multi-objective policies.<n>Our approach jointly learns policies and multiple objective-specific reward models from human feedback, enabling agents to effectively balance competing objectives.
  arXiv  Detail & Related papers  (2026-07-31T15:50:29Z)
• PCHC: Enabling Preference Conditioned Humanoid Control via Multi-Objective Reinforcement Learning [50.63196995993855]
  We propose a novel framework leveraging Multi-Objective Reinforcement Learning (MORL) to achieve Preference-Conditioned Humanoid Control (PCHC)<n>Our framework enables a single, preference-conditioned policy to exhibit a wide spectrum of diverse behaviors.
  arXiv  Detail & Related papers  (2026-03-25T07:55:37Z)
• Polychromic Objectives for Reinforcement Learning [63.37185057794815]
  Reinforcement learning fine-tuning (RLFT) is a dominant paradigm for improving pretrained policies for downstream tasks.<n>We introduce an objective for policy methods that explicitly enforces the exploration and refinement of diverse generations.<n>We show how proximal policy optimization (PPO) can be adapted to optimize this objective.
  arXiv  Detail & Related papers  (2025-09-29T19:32:11Z)
• Preference-based Multi-Objective Reinforcement Learning [5.031225669460861]
  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.
  arXiv  Detail & Related papers  (2025-07-18T16:43:04Z)
• Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning [67.95280175998792]
  A novel adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association ins. We employ inverse RL (IRL) to automatically learn reward functions without manual tuning. We show that the proposed MA-AL method outperforms traditional RL approaches, achieving a $14.6%$ improvement in convergence and reward value.
  arXiv  Detail & Related papers  (2024-09-27T13:05:02Z)
• In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning [0.6650227510403052]
  Multi-objective reinforcement learning (MORL) is essential for addressing the intricacies of real-world RL problems. MORL is challenging due to unstable learning dynamics with deep learning-based function approximators. Our work empirically explores model-free policy learning loss functions and the impact of different architectural choices.
  arXiv  Detail & Related papers  (2024-07-23T19:17:47Z)
• Multi-turn Reinforcement Learning from Preference Human Feedback [41.327438095745315]
  Reinforcement Learning from Human Feedback (RLHF) has become the standard approach for aligning Large Language Models with human preferences.<n>Existing methods work by emulating the preferences at the single decision (turn) level.<n>We develop novel methods for Reinforcement Learning from preference feedback between two full multi-turn conversations.
  arXiv  Detail & Related papers  (2024-05-23T14:53:54Z)
• 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)
• 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)
• MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning [14.06682547001011]
  State-of-the art methods typically focus on learning a single reward model. We propose Multi-Objective Reinforced Active Learning (MORAL), a novel method for combining diverse demonstrations of social norms. Our approach is able to interactively tune a deep RL agent towards a variety of preferences, while eliminating the need for computing multiple policies.
  arXiv  Detail & Related papers  (2021-12-30T19:21:03Z)
• Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning [114.07623388322048]
  We discuss how the standard goal-conditioned RL (GCRL) is encapsulated by the objective variational empowerment. Our work lays a novel foundation from which to evaluate, analyze, and develop representation learning techniques in goal-based RL.
  arXiv  Detail & Related papers  (2021-06-02T18:12:26Z)
• 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)
• Ready Policy One: World Building Through Active Learning [35.358315617358976]
  We introduce Ready Policy One (RP1), a framework that views Model-Based Reinforcement Learning as an active learning problem. RP1 achieves this by utilizing a hybrid objective function, which crucially adapts during optimization. We rigorously evaluate our method on a variety of continuous control tasks, and demonstrate statistically significant gains over existing approaches.
  arXiv  Detail & Related papers  (2020-02-07T09:57:53Z)

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.