Agents That Learn What Matters: A New Approach to World Modeling

Author: Denis Avetisyan

This research introduces a self-supervised framework that allows agents to continuously refine their understanding of the world by inventing new concepts as they learn.

The system decomposes the challenge of navigating a complex environment-specifically, a lava crossing-into a hierarchy of reusable concepts and constraints, demonstrating that abstract notions like ‘moving’ can underpin both progression and terminal states, while learned constraints-such as the mutual exclusion between being alive and dead-enforce a physically consistent, interpretable model of the world [latex]\otimes[/latex].

The system combines logic programming and continuous model repair to create interpretable, symbolic world models capable of online learning and predicate invention.

Standard approaches to world modelling struggle with sample inefficiency and scalability in complex, dynamic environments. This limitation motivates the work presented in ‘Continual learning and refinement of causal models through dynamic predicate invention’, which introduces a novel framework for online learning of symbolic causal models. By integrating continuous model learning with meta-interpretive learning and dynamic predicate invention, the approach enables agents to construct interpretable, hierarchical representations from observations, achieving substantial gains in sample efficiency over existing methods. Can this framework facilitate the development of agents capable of robust, generalizable reasoning in increasingly complex real-world scenarios?

The Inevitable Limits of Scale: Reinforcement Learning’s Core Challenge

Proximal Policy Optimization (PPO), a widely used reinforcement learning algorithm, frequently encounters limitations when tackling tasks demanding extended sequences of actions and intricate environmental dynamics. The core issue stems from the algorithm’s data efficiency; PPO requires an immense volume of interactions with the environment to reliably learn an effective policy. This is because, with each step in a long-horizon task, the potential for cumulative error increases, necessitating constant refinement of the policy through repeated trials. Consequently, applying PPO to realistic scenarios – such as robotics or complex game playing – often proves computationally expensive and time-consuming, as gathering sufficient data for robust learning becomes a significant bottleneck. The sheer scale of data required not only strains computational resources but also hinders the algorithm’s ability to generalize to slightly different conditions or unseen situations.

A significant impediment to deploying reinforcement learning in practical applications stems from its limited ability to generalize. Algorithms meticulously trained within a simulated or highly constrained environment frequently falter when confronted with even slight variations in the real world. This fragility arises because these systems often learn to exploit specific features of the training scenario, rather than developing a deeper understanding of the underlying principles governing the task. Consequently, a robot expertly navigating a pristine laboratory setting might struggle with uneven terrain or unexpected obstacles, or a game-playing AI trained on one version of a game may perform poorly on a slightly modified iteration. This lack of robustness necessitates extensive retraining for each new environment, significantly increasing development costs and hindering the widespread adoption of reinforcement learning technologies.

The efficacy of reinforcement learning hinges on an agent’s ability to accurately model the environment it inhabits, yet constructing a robust and interpretable representation of environmental dynamics presents a significant hurdle. Traditional methods often treat the environment as a ‘black box’, learning to react to stimuli without developing a deeper understanding of cause and effect. This superficial understanding limits generalization; an agent trained in one scenario struggles when faced with even minor variations. Furthermore, without an interpretable model, it becomes difficult to diagnose failures or improve performance systematically. The agent may achieve success through spurious correlations, performing well in training but failing catastrophically in novel situations. Consequently, research is increasingly focused on techniques that prioritize learning underlying principles – the true ‘rules of the game’ – rather than simply memorizing optimal actions for specific states, thereby paving the way for more adaptable and reliable intelligent systems.

Constructing a Symbolic World: The Online MIL Agent Framework

The Online MIL Agent is a self-supervised learning framework designed to acquire a symbolic representation of an environment’s dynamics. This agent operates by continuously observing state transitions resulting from its own actions and uses this data to construct a logical model. The framework learns without explicit reward signals, instead relying on the inherent structure within the sequential data generated through interaction. This allows the agent to build a model that captures relationships between states and actions, effectively creating an internal representation of the environment’s behavior. The agent’s learning process is “online” in that it continuously updates its model with each new observation, enabling adaptation to changing or complex environments.

The Online MIL Agent utilizes First-Order Logic (FOL) to represent the environment’s state and action effects, providing a structured and expressive formalism beyond propositional logic. Specifically, FOL allows for the representation of objects, their properties, and relations between them; for example, “[latex]holding(agent, object)[/latex]” can denote that the agent is holding a specific object. Action effects are modeled as logical rules specifying how the state changes after an action is taken; for example, an action “[latex]pickUp(object)[/latex]” might add “[latex]holding(agent, object)[/latex]” and remove “[latex]onTable(object)[/latex]”. This logical representation facilitates compositional reasoning, enabling the agent to infer new knowledge by combining existing facts and rules, and to plan sequences of actions based on logical consequences of those actions.

The construction of the most general logic program (MGLP) serves as an efficient method for defining the search space during the learning of environmental dynamics. This approach begins with a program that is true under all possible observations, effectively representing maximal uncertainty. The MGLP is then refined through the application of operator learning, where clauses are added or modified based on observed transitions. This contrasts with searching over all possible logic programs, significantly reducing computational complexity by focusing the search on relevant hypotheses. The resulting search space is constrained by the initial MGLP, ensuring that learned dynamics are grounded in observable data and minimizing the risk of overfitting to spurious correlations. This process allows for efficient learning of the underlying dynamics by prioritizing programs that are consistent with observed transitions while remaining as general as possible.

The Cycle of Understanding: Prediction, Verification, and Refinement

The Predict-Verify-Refine cycle forms the core of the framework’s iterative process. Initially, the agent generates a prediction regarding the subsequent state of the environment based on its current model. This prediction is then compared against observed reality to identify discrepancies. Any identified errors – differences between the predicted and actual states – are used to refine the agent’s internal model. This refinement process adjusts the model’s parameters or structure, aiming to reduce future prediction errors and improve the agent’s ability to accurately represent the environment. The cycle repeats continuously, enabling ongoing learning and adaptation.

The Inertia Assumption streamlines the verification stage of the Predict-Verify-Refine cycle by positing that most aspects of a system’s state remain constant between time steps. Instead of exhaustively verifying the entire predicted state against observed reality, verification focuses solely on identifying changes that have occurred. This significantly reduces computational cost and complexity, as the agent only needs to assess the accuracy of its predictions regarding these changes. By isolating and evaluating differences, the system can efficiently pinpoint areas where the model requires refinement, improving performance and reducing the demand for complete state re-evaluation.

Hypothesis refinement within the Predict-Verify-Refine cycle necessitates the identification of both false positive and false negative errors to optimize model performance. False positives occur when the model predicts a state change that does not occur in reality, while false negatives represent instances where a real state change is not predicted. Addressing both error types is crucial; minimizing false positives enhances precision, preventing unnecessary actions or alerts, and reducing false negatives improves recall, ensuring the model doesn’t miss critical changes in the observed environment. The simultaneous consideration of both error types allows for a balanced approach to model adjustment, leading to both accuracy and completeness in its predictive capabilities.

Beyond Simple Objects: The Emergence of Scalable Abstraction

The agent’s capacity for Predicate Invention represents a significant departure from traditional approaches to object representation. Rather than relying on pre-defined attributes, the system dynamically generates new predicates – essentially, conceptual labels – to describe observed relationships and properties. This allows for the construction of hierarchical abstractions; simple predicates can be combined to form more complex ones, building a layered understanding of the environment. For instance, an agent might initially define a predicate for “is_near,” then combine it with “is_red” to invent “red_thing_nearby.” This process isn’t limited to visual characteristics; it extends to functional relationships and abstract concepts, allowing the agent to represent and reason about the world in a highly flexible and scalable manner, ultimately facilitating a more robust understanding of complex scenarios.

The capacity to generalize beyond specific object instances is fundamentally enabled by Lifted Dynamics, a system that prioritizes relationships over individual identities. Rather than processing each object as a unique entity, the agent learns to recognize and reason about how objects interact with one another – whether a block is ‘on’ another, or an object is ‘inside’ a container. This relational reasoning dramatically enhances scalability because the computational complexity doesn’t increase linearly with the number of objects in the environment; the agent can apply learned dynamics to novel combinations of objects without requiring re-learning. Consequently, the agent can efficiently manage increasingly complex scenes and tackle long-horizon tasks that would be intractable if it were forced to process each object as a discrete case, effectively shifting from object-centric to relation-centric representation.

The capacity to generalize learning beyond specific instances represents a pivotal advancement in tackling intricate, long-horizon tasks. Traditional artificial intelligence often struggles with scenarios requiring reasoning about abstract concepts – recognizing that a ‘stack’ exists regardless of whether it’s built from blocks or books, for instance. This limitation hinders performance in complex environments demanding flexible problem-solving. By moving beyond memorization of individual object identities, an agent can instead focus on relational understanding, enabling it to apply previously learned knowledge to novel situations. Consequently, the agent isn’t constrained by the need to re-learn concepts each time the specific objects change, significantly improving its efficiency and adaptability in dynamic, real-world applications that require sustained, abstract thought.

Towards True Adaptability: Evaluation and Future Trajectories

Recent evaluations within the MiniHack environment reveal a substantial improvement in sample efficiency achieved by this novel framework when contrasted with conventional reinforcement learning algorithms. This means the agent requires significantly less trial-and-error – fewer interactions with the environment – to learn effective strategies. By intelligently leveraging symbolic representations, the agent is able to generalize from limited experience, accelerating the learning process and reducing the computational resources needed to achieve proficiency. This enhanced efficiency is particularly crucial for complex tasks and environments where obtaining large datasets through traditional methods is impractical or costly, paving the way for more adaptable and resource-conscious artificial intelligence systems.

The agent’s capacity to construct a symbolic representation of its environment unlocks both interpretability and enhanced generalization. Unlike traditional reinforcement learning approaches that often yield opaque policies, this framework allows for understanding why a particular action was taken, as decisions are grounded in identifiable symbolic concepts. More crucially, this symbolic knowledge isn’t limited to the training domain; the agent demonstrates a remarkable ability to transfer learned concepts to entirely new tasks within the MiniHack environment. This transfer capability stems from the agent’s ability to reason at an abstract level, applying previously acquired knowledge – such as understanding object affordances or spatial relationships – to solve novel challenges without extensive retraining, paving the way for more adaptable and efficient artificial intelligence systems.

The agent’s capacity for rapid adaptation is strikingly demonstrated through its performance in the MiniHack lava environment; it successfully navigated and completed the challenge in a single episode, effectively achieving one-shot learning. This stands in marked contrast to a Proximal Policy Optimization (PPO) baseline, a commonly used reinforcement learning algorithm, which required approximately 128 episodes to reach comparable performance. This substantial difference highlights the efficiency gained through the agent’s symbolic representation of the environment, enabling it to generalize quickly from limited experience and formulate effective strategies with minimal trial and error. The ability to solve complex tasks after just one exposure suggests a significant step toward more generalizable and adaptable artificial intelligence systems.

The pursuit of robust, adaptable intelligence, as demonstrated in this work concerning continual learning and refinement of causal models, echoes a fundamental truth about complex systems. It is not merely about achieving initial competence, but sustaining it through ongoing interaction with a dynamic environment. As Tim Berners-Lee observed, “The web is more a social creation than a technical one.” This highlights the importance of continuous adaptation and evolution. Similarly, the framework detailed here, with its emphasis on online learning and predicate invention, acknowledges that a static model, however well-formed, will inevitably decay in relevance. The system learns to age gracefully, incrementally repairing and refining its understanding of the world – sometimes observing the process of model repair is more valuable than attempting to accelerate initial convergence.

What’s Next?

The pursuit of continually refined symbolic world models, as demonstrated in this work, inevitably confronts the limitations inherent in any attempt to capture dynamic systems with static representation. Predicate invention, while a powerful tool for adaptation, merely postpones the inevitable accrual of representational debt – a form of erosion where the model’s expressive power gradually diverges from the complexities of the environment. The elegance of logic programming provides a temporary bulwark, but even first-order logic struggles with the infinite regress of necessary refinements.

Future work will likely focus on managing this debt, not eliminating it. The field must shift from seeking perfect models to embracing models that age gracefully-systems capable of self-degradation and controlled reconstruction. A crucial direction lies in integrating probabilistic reasoning, allowing the model to acknowledge its own uncertainty and prioritize repairs based on predictive error, rather than striving for complete ontological fidelity.

Ultimately, the true measure of success will not be the lifespan of a particular model, but the efficiency with which a lineage of models can adapt and persist. Uptime, in this context, becomes a rare phase of temporal harmony, a fleeting moment of alignment before the inevitable return to a state of dynamic disrepair and reconstruction. The challenge, then, is not to build immortal models, but to cultivate robust systems of continual renewal.

Original article: https://arxiv.org/pdf/2602.17217.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/