AD-WM: Action-Discriminative World Models for Counterfactual Model Predictive Control

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AD-WM: Action-Discriminative World Models for Counterfactual Model Predictive Control

Latent world models are typically trained to predict factual transitions, whereas model predictive control (MPC) must compare alternative actions from the same state. A model can therefore achieve low factual prediction error yet poorly distinguish candidate actions. We introduce AD-WM, an action-discriminative joint-embedding world model for counterfactual MPC. AD-WM combines residual latent dynamics with predictor-level action-recovery regularization, using inverse dynamics and a normalized re

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