DMWM: Dual-Mind World Model with Long-Term Imagination

Imagination in world models is crucial for enabling agents to learn long-horizon policies in a sample-efficient manner. Existing recurrent state-space model (RSSM)based world models depend on single-step statistical inference to capture the environment dynamics, and, hence, they cannot effectively perform long-term imagination tasks due to the accumulation of prediction errors. Inspired by the dual-process theory of human cognition, we propose a novel dual-mind world model (DMWM) framework that integrates logical reasoning to enable imagination with logical consistency. DMWM is composed of two components: an RSSM-based System 1 (RSSM-S1) component that handles state transitions in an intuitive manner and a logic-integrated neural network-based System 2 (LINN-S2) component that guides the imagination process through hierarchical deep logical reasoning. The intersystem feedback mechanism is designed to ensure that the imagination process follows the logical rules of the real environment. The proposed framework is evaluated on benchmark tasks that require long-term planning from the DMControl suite and the robotic platforms. Extensive experimental results demonstrate that the proposed framework yields significant improvements in terms of logical coherence, trial efficiency, data efficiency and long-term imagination over the state-of-the-art world models. The code is available at https://github.com/news-vt/DMWM.