LacaDM: A Latent Causal Diffusion Model for Multiobjective Reinforcement Learning
This addresses the problem of improving generalization and adaptability in MORL for researchers and practitioners, representing a novel method rather than an incremental advance.
The paper tackled the challenge of multiobjective reinforcement learning (MORL) by introducing LacaDM, a latent causal diffusion model that learns temporal causal relationships to enhance adaptability in discrete and continuous environments, achieving state-of-the-art performance on tasks from the MOGymnasium framework in metrics like hypervolume, sparsity, and expected utility maximization.
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.