World Models

Recently I read lots of texts related to World Models. The starting point was to write a survey, but for various reasons, I decided to summarize some of the materials I read in a blog.

Main reasons of giving up is:

• some researchers do similar work
• The academic community has not yet unified the definition of World Models

The journey of world models from conceptual frameworks in control theory during the 1970s to their current prominence in artificial intelligence research reflects a remarkable trajectory of technological evolution and interdisciplinary fusion. The initial formulations in control theory, as laid out by pioneers, were foundational, setting the stage for the integration of computational models in dynamic system management. These early efforts were instrumental in demonstrating the potential of applying mathematical models to predict and control complex systems, a principle that would eventually underpin the development of world models.

As the field progressed, the advent of neural networks introduced a paradigm shift, allowing for the modeling of dynamic systems with unparalleled depth and complexity. This transition from static, linear models to dynamic, non-linear representations facilitated a deeper understanding of environmental interactions, laying the groundwork for the sophisticated world models we see today. The integration of recurrent neural networks (RNNs) was particularly transformative, marking a leap towards systems capable of temporal data processing, essential for predicting future states and enabling abstract reasoning.

The formal unveiling of world models by Ha and Schmidhuber in 2018 was a defining moment that captured the collective aspiration of the AI research community to endow machines with a level of cognitive processing reminiscent of human consciousness . By harnessing the power of Mixture Density Networks (MDN) and RNNs, this work illuminated the path for unsupervised learning to extract and interpret the spatial and temporal patterns inherent in environmental data. The significance of this breakthrough cannot be overstated, it demonstrated that autonomous systems could achieve a nuanced understanding of their operational environments, predicting future scenarios with an accuracy that was previously unattainable.

More details can be found in World Models for Autonomous Driving: An Initial Survey (arxiv.org), which includes Architectural Foundations of World Models(seems like agents), some frameworks and application, Summary of Recent World Model Applications(a Table).

Meta is doing related work about World Models, you can read their papers.