Limitations of Scalarisation in MORL: A Comparative Study in Discrete Environments

• URL: http://arxiv.org/abs/2511.16476v1
• Date: Thu, 20 Nov 2025 15:45:50 GMT
• Title: Limitations of Scalarisation in MORL: A Comparative Study in Discrete Environments
• Authors: Muhammad Sa'ood Shah, Asad Jeewa,
• Abstract summary: This study examines selected Multi-Objective Reinforcement Learning (MORL) algorithms across discrete action and observation spaces.<n>We use an outer-loop multi-policy methodology to assess the performance of a seminal single-policy MORL algorithm, MO Q-Learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scalarisation functions are widely employed in MORL algorithms to enable intelligent decision-making. However, these functions often struggle to approximate the Pareto front accurately, rendering them unideal in complex, uncertain environments. This study examines selected Multi-Objective Reinforcement Learning (MORL) algorithms across MORL environments with discrete action and observation spaces. We aim to investigate further the limitations associated with scalarisation approaches for decision-making in multi-objective settings. Specifically, we use an outer-loop multi-policy methodology to assess the performance of a seminal single-policy MORL algorithm, MO Q-Learning implemented with linear scalarisation and Chebyshev scalarisation functions. In addition, we explore a pioneering inner-loop multi-policy algorithm, Pareto Q-Learning, which offers a more robust alternative. Our findings reveal that the performance of the scalarisation functions is highly dependent on the environment and the shape of the Pareto front. These functions often fail to retain the solutions uncovered during learning and favour finding solutions in certain regions of the solution space. Moreover, finding the appropriate weight configurations to sample the entire Pareto front is complex, limiting their applicability in uncertain settings. In contrast, inner-loop multi-policy algorithms may provide a more sustainable and generalizable approach and potentially facilitate intelligent decision-making in dynamic and uncertain environments.

Related papers

• Pareto Set Learning for Multi-Objective Reinforcement Learning [19.720934024901542]
  We propose a decomposition-based framework for Multi-Objective RL (MORL)<n>PSL-MORL harnesses the generation capability of hypernetwork to produce the parameters of the policy network for each decomposition weight.<n>We show that PSL-MORL significantly outperforms state-of-the-art MORL methods in the hypervolume and sparsity indicators.
  arXiv  Detail & Related papers  (2025-01-12T10:43:05Z)
• Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational Hypernetworks [4.124390946636935]
  Expensive multi-objective optimization problems (EMOPs) are common in real-world scenarios where evaluating objective functions is costly.<n>We propose a novel approach called SVH-PSL, which integrates Stein Variational Gradient Descent (SVGD) with Hypernetworks.<n>Our method addresses the issues of fragmented surrogate models and pseudo-local optima by collectively moving particles in a manner that smooths out the solution space.
  arXiv  Detail & Related papers  (2024-12-23T06:05:45Z)
• UCB-driven Utility Function Search for Multi-objective Reinforcement Learning [51.00436121587591]
  In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours.<n>We focus on the case of linear utility functions parametrised by weight vectors w.<n>We introduce a method based on Upper Confidence Bound to efficiently search for the most promising weight vectors during different stages of the learning process.
  arXiv  Detail & Related papers  (2024-05-01T09:34:42Z)
• Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods [52.0617030129699]
  We introduce a novel theoretical framework for analyzing the effectiveness of DeepMatching Networks and Reinforcement Learning methods. Our main contribution holds for a broad class of problems including Max-and Min-Cut, Max-$k$-Bipartite-Bi, Maximum-Weight-Bipartite-Bi, and Traveling Salesman Problem. As a byproduct of our analysis we introduce a novel regularization process over vanilla descent and provide theoretical and experimental evidence that it helps address vanishing-gradient issues and escape bad stationary points.
  arXiv  Detail & Related papers  (2023-10-08T23:39:38Z)
• Distributionally Robust Model-based Reinforcement Learning with Large State Spaces [55.14361269378122]
  Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment. We study distributionally robust Markov decision processes with continuous state spaces under the widely used Kullback-Leibler, chi-square, and total variation uncertainty sets. We propose a model-based approach that utilizes Gaussian Processes and the maximum variance reduction algorithm to efficiently learn multi-output nominal transition dynamics.
  arXiv  Detail & Related papers  (2023-09-05T13:42:11Z)
• Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning [0.0]
  Multi-objective reinforcement learning (MORL) is a relatively new field which builds on conventional Reinforcement Learning (RL) This thesis focuses on what factors influence the frequency with which value-based MORL Q-learning algorithms learn the optimal policy for an environment.
  arXiv  Detail & Related papers  (2022-11-16T04:56:42Z)
• Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach [0.3093890460224435]
  We address the solution of the popular Wordle puzzle, using new reinforcement learning methods. For the Wordle puzzle, they yield on-line solution strategies that are very close to optimal at relatively modest computational cost.
  arXiv  Detail & Related papers  (2022-11-15T03:46:41Z)
• Multi-Objective Policy Gradients with Topological Constraints [108.10241442630289]
  We present a new algorithm for a policy gradient in TMDPs by a simple extension of the proximal policy optimization (PPO) algorithm. We demonstrate this on a real-world multiple-objective navigation problem with an arbitrary ordering of objectives both in simulation and on a real robot.
  arXiv  Detail & Related papers  (2022-09-15T07:22:58Z)
• Discovering Diverse Solutions in Deep Reinforcement Learning [84.45686627019408]
  Reinforcement learning algorithms are typically limited to learning a single solution of a specified task. We propose an RL method that can learn infinitely many solutions by training a policy conditioned on a continuous or discrete low-dimensional latent variable.
  arXiv  Detail & Related papers  (2021-03-12T04:54:31Z)
• 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.