Next-embedding prediction enables stronger world models without pixel reconstruction overhead.
Capturing temporal dependencies is critical for model-based reinforcement learning (MBRL) in partially observable, high-dimensional domains. We introduce NE-Dreamer, a decoder-free MBRL agent that leverages a temporal transformer to predict next-step encoder embeddings from latent state sequences, directly optimizing temporal predictive alignment in representation space. This approach enables NE-Dreamer to learn coherent, predictive state representations without reconstruction losses or auxiliary supervision. On the DeepMind Control Suite, NE-Dreamer matches or exceeds the performance of DreamerV3 and leading decoder-free agents. On a challenging subset of DMLab tasks involving memory and spatial reasoning, NE-Dreamer achieves substantial gains. These results establish next-embedding prediction with temporal transformers as an effective, scalable framework for MBRL in complex, partially observable environments.
NE-Dreamer predicts next-step embeddings instead of reconstructing pixels, improving temporal coherence in world models.
The method outperforms DreamerV3 on memory-intensive navigation tasks without pixel reconstruction or data augmentation.
Decoder-free approaches require explicit temporal constraints to maintain predictive representations under partial observability.
Strong performance shown primarily on DMLab memory/navigation tasks; generalization to other domains unclear.
Approach evaluated against limited baselines; comparison with other recent decoder-free methods incomplete.
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