CausalDreamer: Learning Predictive World Models with Latent Disentanglement
CausalDreamer learns predictive world models by re-encoding a frozen video tokenizer's latent representation into four factored groups based on controllability and reward relevance. This disentanglement allows the model to focus planning on controllable and relevant parts of the environment, leading to better performance in clean tasks and manipulated variants.