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LacaDM: A Latent Causal Diffusion Model for Multiobjective Reinforcement Learning

Xueming Yan, Bo Yin, Yaochu Jin

TL;DR

LacaDM tackles MORL generalization by embedding latent temporal causal structures into a diffusion framework, enabling efficient policy generation that balances multiple objectives. The model uses forward diffusion guided by IRL-context embeddings and CRL-informed reverse diffusion to refine policies, with an explicit CRL-driven loss that enforces causal coherence. Empirical results on MO-Gymnasium show LacaDM outperforms state-of-the-art baselines in hypervolume, sparsity, and expected utility, across both discrete and continuous tasks, and an ablation confirms the critical role of CRL for transfer and adaptation. This approach offers a scalable, data-efficient path to robust multiobjective decision-making in dynamic environments.

Abstract

Multiobjective reinforcement learning (MORL) poses significant challenges due to the inherent conflicts between objectives and the difficulty of adapting to dynamic environments. Traditional methods often struggle to generalize effectively, particularly in large and complex state-action spaces. To address these limitations, we introduce the Latent Causal Diffusion Model (LacaDM), a novel approach designed to enhance the adaptability of MORL in discrete and continuous environments. Unlike existing methods that primarily address conflicts between objectives, LacaDM learns latent temporal causal relationships between environmental states and policies, enabling efficient knowledge transfer across diverse MORL scenarios. By embedding these causal structures within a diffusion model-based framework, LacaDM achieves a balance between conflicting objectives while maintaining strong generalization capabilities in previously unseen environments. Empirical evaluations on various tasks from the MOGymnasium framework demonstrate that LacaDM consistently outperforms the state-of-art baselines in terms of hypervolume, sparsity, and expected utility maximization, showcasing its effectiveness in complex multiobjective tasks.

LacaDM: A Latent Causal Diffusion Model for Multiobjective Reinforcement Learning

TL;DR

LacaDM tackles MORL generalization by embedding latent temporal causal structures into a diffusion framework, enabling efficient policy generation that balances multiple objectives. The model uses forward diffusion guided by IRL-context embeddings and CRL-informed reverse diffusion to refine policies, with an explicit CRL-driven loss that enforces causal coherence. Empirical results on MO-Gymnasium show LacaDM outperforms state-of-the-art baselines in hypervolume, sparsity, and expected utility, across both discrete and continuous tasks, and an ablation confirms the critical role of CRL for transfer and adaptation. This approach offers a scalable, data-efficient path to robust multiobjective decision-making in dynamic environments.

Abstract

Multiobjective reinforcement learning (MORL) poses significant challenges due to the inherent conflicts between objectives and the difficulty of adapting to dynamic environments. Traditional methods often struggle to generalize effectively, particularly in large and complex state-action spaces. To address these limitations, we introduce the Latent Causal Diffusion Model (LacaDM), a novel approach designed to enhance the adaptability of MORL in discrete and continuous environments. Unlike existing methods that primarily address conflicts between objectives, LacaDM learns latent temporal causal relationships between environmental states and policies, enabling efficient knowledge transfer across diverse MORL scenarios. By embedding these causal structures within a diffusion model-based framework, LacaDM achieves a balance between conflicting objectives while maintaining strong generalization capabilities in previously unseen environments. Empirical evaluations on various tasks from the MOGymnasium framework demonstrate that LacaDM consistently outperforms the state-of-art baselines in terms of hypervolume, sparsity, and expected utility maximization, showcasing its effectiveness in complex multiobjective tasks.
Paper Structure (23 sections, 20 equations, 3 figures, 3 tables)

This paper contains 23 sections, 20 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Diffusion Probabilistic Models
  4. Continuous diffusion models.
  5. Discrete diffusion models.
  6. Multiobjective Reinforcement Learning
  7. Causal Representation Learning for MORL
  8. Methodology
  9. Overview
  10. Forward Diffusion for Noise Estimation
  11. Generation of Optimal Policies via Reverse Diffusion
  12. Incorporating causal learning.
  13. CRL-guided policy update.
  14. Experiments
  15. Experiment Setup
  16. ...and 8 more sections

Figures (3)

  • Figure 1: Overview of the proposed LacaDM. Inverse RL-guided context embeddings derived from PCN-generated trajectories guide the forward diffusion from optimal solutions to random noise. Latent causal variables $z_t$, learned via CRL, support reverse denoising to recover high-quality policy solutions.
  • Figure 2: Expected utility of baseline models and LacaDM across four MORL problems as the number of solving steps increases.
  • Figure 3: Cosine similarity heatmaps comparing noise inference and training between LacaDM-CRL and LacaDM at the midpoint of the diffusion process.