Integrating Action Prefix Prediction and Alignment Across Modalities for Multimodal Reinforcement Learning

arXiv:2611.13782v1 Announce Type: new Abstract: Learning representations from multimodal observations is difficult in reinforcement learning with limited interaction data. Multimodal Joint Predictive Representation combines multimodal latent prediction with contrastive learning across modalities, but its recursive rollout depends on earlier predictions and its pairwise alignment focuses on correspondence between modalities. We propose Improved Predictive Representation, which combines prediction from action prefixes with alignment at the observed anchor based on Contrastive Multimodal learning

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