On Generalization Across Environments In Multi-Objective Reinforcement Learning

• URL: http://arxiv.org/abs/2503.00799v2
• Date: Wed, 12 Mar 2025 23:09:08 GMT
• Title: On Generalization Across Environments In Multi-Objective Reinforcement Learning
• Authors: Jayden Teoh, Pradeep Varakantham, Peter Vamplew,
• Abstract summary: We formalize the concept of generalization in Multi-Objective Reinforcement Learning (MORL) and how it can be evaluated.<n>We contribute a novel benchmark featuring diverse multi-objective domains with parameterized environment configurations.<n>Our baseline evaluations of state-of-the-art MORL algorithms on this benchmark reveals limited generalization capabilities, suggesting significant room for improvement.
• Score: 6.686583184622338
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world sequential decision-making tasks often require balancing trade-offs between multiple conflicting objectives, making Multi-Objective Reinforcement Learning (MORL) an increasingly prominent field of research. Despite recent advances, existing MORL literature has narrowly focused on performance within static environments, neglecting the importance of generalizing across diverse settings. Conversely, existing research on generalization in RL has always assumed scalar rewards, overlooking the inherent multi-objectivity of real-world problems. Generalization in the multi-objective context is fundamentally more challenging, as it requires learning a Pareto set of policies addressing varying preferences across multiple objectives. In this paper, we formalize the concept of generalization in MORL and how it can be evaluated. We then contribute a novel benchmark featuring diverse multi-objective domains with parameterized environment configurations to facilitate future studies in this area. Our baseline evaluations of state-of-the-art MORL algorithms on this benchmark reveals limited generalization capabilities, suggesting significant room for improvement. Our empirical findings also expose limitations in the expressivity of scalar rewards, emphasizing the need for multi-objective specifications to achieve effective generalization. We further analyzed the algorithmic complexities within current MORL approaches that could impede the transfer in performance from the single- to multiple-environment settings. This work fills a critical gap and lays the groundwork for future research that brings together two key areas in reinforcement learning: solving multi-objective decision-making problems and generalizing across diverse environments. We make our code available at https://github.com/JaydenTeoh/MORL-Generalization.

Related papers

• Continual Learning Should Move Beyond Incremental Classification [51.23416308775444]
  Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. Here, we argue that maintaining such a focus limits both theoretical development and practical applicability of CL methods. We identify three fundamental challenges: (C1) the nature of continuity in learning problems, (C2) the choice of appropriate spaces and metrics for measuring similarity, and (C3) the role of learning objectives beyond classification.
  arXiv  Detail & Related papers  (2025-02-17T15:40:13Z)
• Towards Modality Generalization: A Benchmark and Prospective Analysis [56.84045461854789]
  This paper introduces Modality Generalization (MG), which focuses on enabling models to generalize to unseen modalities.<n>We propose a comprehensive benchmark featuring multi-modal algorithms and adapt existing methods that focus on generalization.<n>Our work provides a foundation for advancing robust and adaptable multi-modal models, enabling them to handle unseen modalities in realistic scenarios.
  arXiv  Detail & Related papers  (2024-12-24T08:38:35Z)
• Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning [10.848218400641466]
  Multi-objective reinforcement learning (MORL) is used to solve problems involving multiple objectives. We propose an approach for clustering the solution set generated by MORL.
  arXiv  Detail & Related papers  (2024-11-07T15:26:38Z)
• 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)
• MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning [7.822825134714791]
  Multi-objective multi-agent reinforcement learning (MOMARL) addresses problems with multiple agents each needing to consider multiple objectives in their learning process. MOAland is the first collection of standardised environments for multi-objective multi-agent reinforcement learning.
  arXiv  Detail & Related papers  (2024-07-23T09:05:06Z)
• Coding for Intelligence from the Perspective of Category [66.14012258680992]
  Coding targets compressing and reconstructing data, and intelligence. Recent trends demonstrate the potential homogeneity of these two fields. We propose a novel problem of Coding for Intelligence from the category theory view.
  arXiv  Detail & Related papers  (2024-07-01T07:05:44Z)
• Scalable Language Model with Generalized Continual Learning [58.700439919096155]
  The Joint Adaptive Re-ization (JARe) is integrated with Dynamic Task-related Knowledge Retrieval (DTKR) to enable adaptive adjustment of language models based on specific downstream tasks. Our method demonstrates state-of-the-art performance on diverse backbones and benchmarks, achieving effective continual learning in both full-set and few-shot scenarios with minimal forgetting.
  arXiv  Detail & Related papers  (2024-04-11T04:22:15Z)
• Rethinking Multi-domain Generalization with A General Learning Objective [17.155829981870045]
  Multi-domain generalization (mDG) is universally aimed to minimize discrepancy between training and testing distributions.<n>Existing mDG literature lacks a general learning objective paradigm.<n>We propose to leverage a $Y$-mapping to relax the constraint.
  arXiv  Detail & Related papers  (2024-02-29T05:00:30Z)
• Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework [0.3069335774032178]
  Multi-objective reinforcement learning (MORL) extends traditional RL by seeking policies making different compromises among conflicting objectives. A clear categorization based on both RL and MOO/D is lacking in the existing literature. A comprehensive taxonomy for MORL/D is presented, providing a structured foundation for categorizing existing and potential MORL works. The introduced taxonomy is then used to scrutinize MORL research, enhancing clarity and conciseness through well-defined categorization.
  arXiv  Detail & Related papers  (2023-11-21T10:11:19Z)
• High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning [112.51498431119616]
  This paper studies efficient representation learning for high-modality scenarios involving a large set of diverse modalities. A single model, HighMMT, scales up to 10 modalities (text, image, audio, video, sensors, proprioception, speech, time-series, sets, and tables) and 15 tasks from 5 research areas.
  arXiv  Detail & Related papers  (2022-03-02T18:56:20Z)
• 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)
• Invariant Causal Prediction for Block MDPs [106.63346115341862]
  Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges. We propose a method of invariant prediction to learn model-irrelevance state abstractions (MISA) that generalize to novel observations in the multi-environment setting.
  arXiv  Detail & Related papers  (2020-03-12T21:03:01Z)

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.