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A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
Multi-objective reinforcement learningreward-free reinforcement learning
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.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes leveraging reward-free reinforcement learning (RFRL) as an auxiliary training objective for multi-objective RL, introducing a preference-guided exploration strategy and an auxiliary Q loss. It resides in the 'Pareto-Optimal Policy Set Learning' leaf, which contains four papers including the original work. This leaf sits within the broader 'Preference-Free Policy Learning' branch, indicating a moderately populated research direction focused on discovering diverse Pareto-optimal solutions without fixed preference assumptions during training. The taxonomy shows this is one of several active approaches to handling unknown user preferences, alongside preference modeling, scalarization, and conditioned policy networks.

The paper's leaf neighbors include methods like 'Generalized Algorithm MORL' and 'Traversing Pareto Optimal,' which also target Pareto frontier coverage but differ in their exploration mechanisms. Adjacent branches reveal alternative paradigms: 'Conditioned Policy Networks' (four papers) train single policies conditioned on preference vectors, while 'Scalarization and Aggregation Methods' (three papers) combine objectives into scalar rewards. The taxonomy's 'Dynamic and Adaptive Preference Handling' branch (six papers) addresses time-varying preferences, a complementary challenge. The paper's RFRL perspective bridges preference-free learning with exploration strategies, positioning it at the intersection of policy set discovery and efficient objective space coverage.

Among 30 candidates examined, none clearly refute the three core contributions. Contribution A (RFRL perspective for MORL) examined 10 candidates with zero refutable matches, suggesting limited prior work explicitly connecting RFRL training objectives to multi-objective settings within this search scope. Contribution B (preference-guided exploration) and Contribution C (auxiliary Q loss) each examined 10 candidates with zero refutations, indicating these specific mechanisms appear distinct from the examined literature. However, the search scope is constrained to top-K semantic matches and citation expansion, not an exhaustive survey. The absence of refutations reflects the limited candidate pool rather than definitive novelty across the entire field.

Based on the limited search scope of 30 candidates, the work appears to introduce a relatively unexplored angle by adapting RFRL training objectives to MORL, particularly through preference-guided exploration. The taxonomy context shows the paper occupies a moderately active research area (four papers in its leaf, 50 total in the taxonomy), suggesting room for methodological innovation. The analysis cannot confirm whether similar RFRL-MORL connections exist in work outside the examined candidates, and the contribution-level statistics reflect search limitations rather than comprehensive field coverage.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Multi-objective reinforcement learning with unknown user preferences. The field addresses scenarios where an agent must balance multiple, often conflicting objectives without explicit knowledge of how a user values trade-offs among them. The taxonomy organizes research into several main branches: Preference Modeling and Inference Approaches focus on learning or estimating user preferences from feedback or demonstrations (e.g., Inferring Preferences Demonstrations[47], Dynamic Preference Inference[17]); Dynamic and Adaptive Preference Handling tackles settings where preferences shift over time (e.g., Robust Dynamic Preferences[1], Continual via Rehearsal[3]); Preference-Free Policy Learning aims to discover diverse Pareto-optimal solutions without assuming a fixed preference vector (e.g., Generalized Algorithm MORL[13], Traversing Pareto Optimal[35]); Scalarization and Aggregation Methods combine objectives into scalar rewards using weighted sums or other functions (e.g., TOPSIS Q-learning[10], Model-based Unknown Weights[4]); Conditioned Policy Networks train policies that can be conditioned on preference parameters at deployment (e.g., Rewards-in-Context[5], Preference-Controlled Text Generation[9]); and Application-Driven branches apply these ideas to domains like autonomous driving, smart grids, and recommendation systems (e.g., Adaptable Autonomous Driving[21], Multi-Microgrid Smart Grid[14]). A particularly active line of work explores how to build rich sets of Pareto-optimal policies that cover the entire trade-off frontier, enabling post-hoc preference specification or rapid adaptation. Reward-Free Multi-Objective[0] exemplifies this direction by learning a diverse policy repertoire without requiring any preference information during training, closely aligning with the Preference-Free Policy Learning branch. This contrasts with methods like Continual via Rehearsal[3], which incrementally updates policies as new preference regions are encountered, and with Traversing Pareto Optimal[35], which focuses on efficiently navigating the Pareto front once it has been approximated. Meanwhile, approaches such as Rewards-in-Context[5] and Preference-Controlled Text Generation[9] emphasize conditioning mechanisms that allow a single model to adapt at inference time, trading off the need for exhaustive coverage against the flexibility of runtime control. The central tension across these branches lies in balancing exploration of the objective space, sample efficiency, and the ability to respond to previously unseen or dynamically changing user preferences.

Claimed Contributions

Reward-free reinforcement learning perspective for multi-objective RL

The authors establish a novel conceptual connection showing that MORL can be viewed as a special case of RFRL. This perspective motivates using RFRL's training objective as auxiliary tasks to enhance MORL by enabling knowledge sharing beyond preference-weighted reward functions.

10 retrieved papers
Preference-guided exploration strategy for adapting RFRL to MORL

The authors introduce a preference-guided exploration method (PG-Explore) that constructs latent vector distributions via mini-batch sampling guided by preference-weighted rewards. This addresses the inefficiency of purely reward-free exploration by directing the agent to visit states relevant to MORL tasks.

10 retrieved papers
Auxiliary Q loss for learning from observed reward vectors

The authors propose an auxiliary Q loss that enables the forward-backward representations to learn directly from observed multi-objective reward vectors rather than pseudo rewards. This provides an additional learning signal specifically designed to adapt RFRL methods to the MORL setting.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Reward-free reinforcement learning perspective for multi-objective RL

The authors establish a novel conceptual connection showing that MORL can be viewed as a special case of RFRL. This perspective motivates using RFRL's training objective as auxiliary tasks to enhance MORL by enabling knowledge sharing beyond preference-weighted reward functions.

Contribution

Preference-guided exploration strategy for adapting RFRL to MORL

The authors introduce a preference-guided exploration method (PG-Explore) that constructs latent vector distributions via mini-batch sampling guided by preference-weighted rewards. This addresses the inefficiency of purely reward-free exploration by directing the agent to visit states relevant to MORL tasks.

Contribution

Auxiliary Q loss for learning from observed reward vectors

The authors propose an auxiliary Q loss that enables the forward-backward representations to learn directly from observed multi-objective reward vectors rather than pseudo rewards. This provides an additional learning signal specifically designed to adapt RFRL methods to the MORL setting.

Table of Contents