Title: Value-aligned World Model Regularization for Model-based Reinforcement Learning
Primary area: reinforcement learning
Keywords: Model-based reinforcement learning; World model; Value-aligned Regularization

Abstract: Model-based reinforcement learning (MBRL) aims to construct world models for imagined interactions to enable efficient sampling. Based on training strategy, current mainstream algorithms can be categorized into two types: maximum likelihood and value-aware world models. The former adopts structured Recurrent/Transformer State-Space Models (RSSM/TSSM) to capture environmental dynamics but may overlook task-relevant features. The latter focuses on decision-critical states by minimizing one-step value evaluations, but it often obtains sub-optimal performance and is difficult to scale. Recent work has attempted to integrate these approaches by leveraging the strong priors of pre-trained large models, though at the cost of increased computational complexity. In this work, we focus on combining these two approaches with minimal modifications. We empirically demonstrate that the key to their integration lies in: RSSM/TSSM ensuring the lower bound of the world model, while value awareness enhances the upper bound. To this end, we introduce a value-alignment regularization term into the maximum likelihood world model learning, promoting task-aware feature reconstruction while modeling the stochastic dynamics. To stabilize training, we propose a warm-up phase and an adaptive weight mechanism for value-representation balance. Extensive experiments across 46 environments from the Atari 100k and DeepMind Control Suite benchmarks, covering both continuous and discrete action control tasks with visual and proprioceptive vector inputs, show that our algorithm consistently boosts existing MBRL methods performance and convergence speed with minimal additional code and computational complexity.