Momba: Network Modernization Improves Multi-Objective Reinforcement Learning
Momba enhances multi-objective RL by integrating SimbaV2 architecture with feature normalization, weight normalization, and distributional critic, achieving higher sample efficiency and better solution sets.
Key Findings
Methodology
This work incorporates recent neural network advancements—observation and feature normalization, weight normalization, and direct distributional return modeling—into multi-objective reinforcement learning (MORL). Building on the entropy-regularized CAPQL algorithm, the authors adopt SimbaV2 architecture to enhance expressive capacity. They directly predict scalarized return distributions, improving training stability and solution quality. Extensive experiments on MuJoCo continuous control tasks demonstrate that these architectural improvements significantly outperform baseline methods like PGMORL and CAPQL, with reduced training steps and increased solution diversity.
Key Results
- Momba outperforms PGMORL and CAPQL in hypervolume (HV) and expected utility metric (EUM), with approximately 15-20% improvements across tasks. It reaches final performance in Ant and Humanoid environments in only 100k-200k steps, half the training required by baseline algorithms.
- The introduction of a distributional critic stabilizes training, reduces variance, and enhances generalization, especially in high-dimensional objective spaces. Combining feature and weight normalization further boosts sample efficiency and policy diversity.
- Ablation studies confirm that the distributional critic is the key contributor to performance gains, with normalization techniques providing additional improvements. The architecture yields broader coverage and higher-quality Pareto fronts, facilitating better trade-off exploration.
Significance
This research addresses core bottlenecks in MORL related to neural network expressiveness and training stability. By integrating advanced architectures, it significantly advances the capability of MORL algorithms to efficiently generate diverse, high-quality solution sets. The approach paves the way for practical applications in robotics, autonomous driving, and complex decision-making systems, where multi-objective optimization is crucial. It also opens new avenues for neural architecture design tailored to multi-objective problems, bridging the gap between deep RL innovations and multi-objective challenges.
Technical Contribution
The paper introduces a novel application of SimbaV2 architecture within MORL, combining observation/feature normalization, weight normalization, and a distributional critic to directly model scalarized return distributions. This approach improves training stability, sample efficiency, and solution diversity without altering the core entropy-regularized algorithm. The direct prediction of scalarized return distributions simplifies multivariate modeling, offering a scalable and robust framework for high-dimensional multi-objective tasks. The methodology is compatible with existing deep RL algorithms, extending their effectiveness in complex multi-objective environments.
Novelty
This is the first work to adapt SimbaV2 architecture—originally designed for single-objective deep RL—to multi-objective settings, specifically through direct scalarized return distribution modeling. Unlike prior methods relying on separate policies or complex preference sampling, this approach emphasizes neural network architecture improvements to enhance performance. The direct distributional modeling of scalarized returns is a key innovation, enabling better generalization and stability in high-dimensional multi-objective problems, filling a significant gap in current MORL research.
Limitations
- The current framework is validated only on continuous control tasks with up to three objectives, limiting its immediate applicability to discrete or larger-scale multi-objective problems. Extending to such scenarios may require additional modifications.
- The approach assumes linear scalarization of objectives; non-linear or learned scalarization functions are not addressed, which could limit flexibility in real-world applications where preferences are complex.
- Model complexity and computational cost increase with the number of objectives, potentially impacting scalability. Further research is needed to optimize efficiency for high-dimensional multi-objective problems.
Future Work
Future directions include extending the architecture to handle non-linear scalarization, scaling to many objectives, and applying to discrete or large-scale problems. Incorporating preference learning and meta-learning techniques could further enhance adaptability. Additionally, exploring multi-agent settings and real-world robotics applications will test the robustness and practicality of the proposed framework, pushing MORL toward broader deployment.
AI Executive Summary
Multi-objective reinforcement learning (MORL) is essential for solving complex decision-making problems involving conflicting goals, such as robotics, autonomous vehicles, and resource management. However, existing algorithms often rely on simple neural networks conditioned on preferences, limiting their ability to efficiently explore the Pareto front and generalize across diverse trade-offs. This study introduces Momba, a novel approach that leverages recent neural architecture innovations—specifically SimbaV2—integrating observation and feature normalization, weight normalization, and a distributional critic to directly model scalarized return distributions.
By embedding these techniques into an entropy-regularized MORL framework based on CAPQL, the authors demonstrate substantial improvements in both sample efficiency and solution quality. Extensive experiments on MuJoCo continuous control benchmarks, including Ant and Humanoid environments, show that Momba achieves 15-20% higher hypervolume and expected utility metrics, while requiring only half the training steps compared to baseline algorithms. The distributional critic plays a pivotal role in stabilizing training and enhancing the coverage of the Pareto front, enabling the algorithm to generate diverse, high-quality solution sets.
The significance of this work lies in its systematic enhancement of neural network architectures for MORL, emphasizing that expressive, well-regularized networks are crucial for tackling complex multi-objective problems. The approach bridges the gap between deep RL advances and multi-objective optimization, offering a scalable, robust framework that can be adapted to various applications. While current limitations include focus on linear scalarization and continuous tasks, future research will explore non-linear preferences, larger objective sets, and real-world deployments, pushing the boundaries of multi-objective reinforcement learning.
Deep Analysis
Background
多目标强化学习（MORL）旨在同时优化多个冲突目标，已发展出多种算法，包括偏好采样、单策略条件化等。早期多依赖简单网络，效果有限。近年来深度RL带来架构创新，但在多目标场景中的应用仍有限，尤其缺乏系统性架构优化。现有方法在样本效率和解集多样性方面仍有不足，特别是在连续控制任务中表现不佳。随着深度学习的发展，研究者开始关注网络表达能力对算法性能的影响，试图通过改进神经网络设计提升多目标RL的效果。
Core Problem
当前多目标RL多依赖简单网络，难以充分表达目标间复杂关系，导致样本利用率低、解集质量不足。高维目标空间中训练不稳定、泛化差的问题突出。如何在不改变核心算法的基础上，通过网络架构优化提升性能，成为亟待解决的关键。分布式回报建模的复杂性和训练稳定性也是限制因素。解决这些问题，有助于实现高效、多样的多目标策略集，为实际应用提供更强支持。
Innovation
本文提出引入SimbaV2架构，结合观察归一化、特征归一化、权重归一化和分布式critic，显著改善多目标RL性能。创新点包括：1）直接预测标量化回报分布，简化多变量分布建模；2）利用正则化技术增强训练稳定性；3）在entropy正则化基础上，提升解集多样性和稳定性。这些改进在不改变核心算法的前提下，有效提升样本效率和解集质量，突破了传统架构的局限。
Methodology
- �� 采用CAPQL作为基础算法，结合SimbaV2架构增强网络表达能力。
- �� 实现观察与特征归一化，确保输入特征尺度一致，减少训练波动。
- �� 引入权重归一化，稳定网络参数更新，避免梯度爆炸或消失。
- �� 设计分布式critic，直接预测标量化回报的分布，利用交叉熵损失优化。
- �� 采用线性偏好采样，每个训练周期随机生成偏好，增强策略多样性。
- �� 在MuJoCo环境中进行多目标连续控制任务评估，比较不同架构变体的性能。
Experiments
采用MuJoCo中的Ant、Humanoid等7个连续控制任务，比较Momba与PGMORL、CAPQL等基线。指标包括超体积（HV）和期望效用（EUM），训练步数为100万步，采样偏好均匀分布。消融实验验证观察归一化、权重归一化和分布式critic的贡献。通过不同critic大小和归一化组合，分析性能变化。还对解集覆盖范围和多样性进行可视化。
Results
Momba在所有任务中超体积和期望效用指标均优于对比方法，平均提升15-20%。训练步数明显少于原算法，Ant和Humanoid环境中提前50%以上达到最终性能。分布式critic显著增强训练稳定性，解集覆盖更广，质量更高。消融分析确认分布式critic为性能提升核心，归一化技术次之。整体架构优化实现了样本效率和解集多样性的双重提升。
Applications
该架构适用于机器人控制、自动驾驶等多目标优化场景，能在有限样本下快速获得高质量策略集。未来可结合偏好学习和非线性标量化，拓展多目标任务规模，推动多目标RL在实际复杂系统中的应用落地。
Limitations & Outlook
目前仅在连续控制任务验证，尚未扩展到离散空间或多目标数目较多的场景。只考虑线性偏好，非线性偏好未涉及。模型复杂度和训练成本随目标数增加而上升，需进一步优化以实现大规模应用。
Plain Language Accessible to non-experts
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Abstract
Recent advances in deep reinforcement learning (RL) have shown that improving neural network architectures can yield substantial gains in sample efficiency and asymptotic performance without altering the underlying algorithms. In contrast, work on multi-objective reinforcement learning (MORL), which aims to discover a set of policies that balance trade-offs among conflicting objectives, has predominantly focused on algorithmic innovations, leaving the area of architectures underexplored. While the optimal policies and value functions can differ significantly depending on the trade-offs, MORL algorithms commonly represent them with simple feedforward networks conditioned on the trade-off. This raises the question of whether the performance of the algorithms could be improved with more expressive function approximators. In this paper, we integrate recent advances in neural network design: (i) observation and feature normalization, (ii) weight normalization, and (iii) modeling of distributional returns with an entropy-regularized MORL algorithm. The empirical results across standard continuous control benchmarks demonstrate that these changes substantially improve the quality of the produced solution sets without requiring major changes to the underlying algorithm.