LacaDM: A Latent Causal Diffusion Model for Multiobjective Reinforcement Learning

2025-12-22

AI Summary

Multi-objective reinforcement learning (MORL) faces challenges including severe objective conflicts, poor adaptability to dynamic environments, and difficulty generalizing in high-dimensional state-action spaces. To address these, we propose LacaDM—a latent causal diffusion model—that pioneers the integration of latent temporal causal inference into the denoising diffusion probabilistic model (DDPM) framework. By explicitly modeling latent causal relationships between states and policies, LacaDM enables cross-scenario knowledge transfer and joint Pareto-optimal policy optimization. Our method synergistically combines latent variable modeling, temporal causal discovery, and multi-objective front optimization. Evaluated on the MOGymnasium benchmark, LacaDM achieves significant improvements: +23.6% in hypervolume, +18.4% in sparsity, higher expected utility, and a 31.2% increase in success rate on unseen environments—demonstrating the effectiveness and advancement of causally grounded generalizable policy learning.

Technology Category

• Machine Learning: Causal Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Enhances adaptability in multiobjective reinforcement learning environments
• Learns latent causal relationships for efficient knowledge transfer
• Balances conflicting objectives while maintaining strong generalization capabilities

Innovation

• Latent causal diffusion model for multiobjective reinforcement learning
• Learns latent temporal causal relationships for knowledge transfer
• Balances conflicting objectives with diffusion model framework