Language-Guided World Models: A Model-Based Approach to AI Control (2402.01695v3)

Abstract: This paper introduces the concept of Language-Guided World Models (LWMs) -- probabilistic models that can simulate environments by reading texts. Agents equipped with these models provide humans with more extensive and efficient control, allowing them to simultaneously alter agent behaviors in multiple tasks via natural verbal communication. In this work, we take initial steps in developing robust LWMs that can generalize to compositionally novel language descriptions. We design a challenging world modeling benchmark based on the game of MESSENGER (Hanjie et al., 2021), featuring evaluation settings that require varying degrees of compositional generalization. Our experiments reveal the lack of generalizability of the state-of-the-art Transformer model, as it offers marginal improvements in simulation quality over a no-text baseline. We devise a more robust model by fusing the Transformer with the EMMA attention mechanism (Hanjie et al., 2021). Our model substantially outperforms the Transformer and approaches the performance of a model with an oracle semantic parsing and grounding capability. To demonstrate the practicality of this model in improving AI safety and transparency, we simulate a scenario in which the model enables an agent to present plans to a human before execution, and to revise plans based on their language feedback.

• The paper demonstrates that language-guided world models substantially improve agent performance, achieving up to a threefold increase.
• The methodology integrates natural language cues with environment dynamics to reduce the need for costly real-world interactions.
• A new Messenger benchmark highlights the limitations of Transformer models, motivating a robust, language-informed architectural redesign.

An Overview of Language-Guided World Models for AI Control

The paper introduces an innovative approach to artificial intelligence control, focusing on the integration of language-guided world models (LWMs) into model-based agents. The motivation stems from the challenges faced by traditional world models, which often lack an intuitive communication interface, thus hindering effective human-agent interaction. LWMs are designed to capture environment dynamics by interpreting language descriptions, thereby enhancing the efficiency and adaptability of agent communication through language feedback. This integration facilitates a significant reduction in the need for interactive experiences in real environments, thereby enhancing safety and efficiency.

A standout contribution of the paper is the development of a challenging benchmark built on the game of Messenger. This benchmark requires agents to exhibit compositional generalization to new language inputs and environment dynamics. The experimental results highlight the limitations of state-of-the-art Transformer architectures on the benchmark and motivate the proposal of a more robust architecture that effectively incorporates language information.

The practical implications of LWMs are substantial. By allowing agents to read language cues, these models open up new avenues for enhancing their interpretability and safety. The paper demonstrates the augmented performance of these models through a scenario where agents generate and discuss plans with a human supervisor before execution. Remarkably, the models achieve up to a threefold improvement in agent performance without requiring any additional interactive data from the environment.

Key Findings and Contributions:

• The strong numerical results underscore that LWMs can significantly enhance the performance of model-based agents. Specifically, LWM-equipped agents reported up to three times better performance compared to those without language guidance, which is a significant technical milestone.
• The paper claims that LWMs allow easy adaptability through natural language, thereby reducing the costly need for real-world interactions. This feature emphasizes a practical advancement in the control of AI agents, as it implies a potential reduction in the tedious process of manual data collection.
• A critical evaluation through the Messenger benchmark reveals the bottleneck in existing Transformer-based models concerning compositional generalization, emphasizing the architectural novelty introduced by the authors.

Implications and Future Directions:

From a theoretical standpoint, this research augments the concept of communication and interaction within model-based learning paradigms. By parameterizing aspects of the world model to be receptive to linguistic input, the research sets a stage for a paradigm shift where policy and world model updates are not purely reliant on state-action pairings but can incorporate complex semantic instruction.

In a practical context, this approach can simplify the control structures of AI agents significantly, especially in complex, dynamic environments where pre-programmed policies might be insufficient. The ability to update internal models of an agent through concise human guidance can lead to more adaptable and generalized AI that requires less direct intervention.

Looking forward, the development of LWMs paves the way for further exploration into AI systems that better understand and interact with human narratives. The focus could shift towards refining these models to process high-dimensional and less structured inputs effectively. Future research could explore more complex environments, transferring these concepts beyond controlled grid-world settings to real-world applications, potentially revolutionizing industries reliant on robotic or autonomous systems. This endeavor opens up substantial research potential in rendering AI processes more intuitive and naturally guided while maintaining robust control performance.