The authors tackle a problem in model-based reinforcement learning: standard world models learn correlations between actions and outcomes but ignore whether an action is actually executable right now (its preconditions). They propose AGWM, which adds an explicit affordance structure—a directed acyclic graph tracking which actions become available or blocked as the agent changes the environment—so the model can track whether each action is currently executable, reducing compounding prediction errors over multi-step rollouts.
Main takeaways:
- Standard world models confuse correlation with causation: if action A and outcome B co-occur frequently in training, the model assumes A always causes B, ignoring prerequisites.
- In interactive environments, actions can change what's possible next ("structure-changing events")—e.g., unlocking a door makes "walk through door" executable.
- AGWM adds a directed acyclic graph that explicitly tracks prerequisite dependencies, so the model knows which actions are executable at each timestep.
- This reduces multi-step prediction error, improves generalization to novel environment configurations, and makes the model more interpretable.
- Tested in game-based simulated environments where action availability changes dynamically.