Dynamics-Aligned Latent Imagination in Contextual World Models for Zero-Shot Generalization

Understand

Real-world reinforcement learning demands adaptation to unseen environmental conditions without costly retraining.

• Contextual Markov Decision Processes (cMDP) model this challenge, but existing methods often require explicit context variables (e.g., friction, gravity), limiting their use when contexts are latent or hard to measure.
• We introduce Dynamics-Aligned Latent Imagination (DALI), a framework integrated within the Dreamer architecture that infers latent context representations from agent-environment interactions.
• By training a self-supervised encoder to predict forward dynamics, DALI generates actionable representations conditioning the world model and policy, bridging perception and control.