Next Embedding Prediction Makes World Models Stronger
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.
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George Bredis
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2026-03-05T09:59:50.299Z
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Natyren
GeorgeBredis
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GeorgeBredis
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Nikita Balagansky
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2026-03-05T09:59:43.757Z
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Nikita Balagansky
elephantmipt
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elephantmipt
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Daniil Gavrilov
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2026-03-05T09:59:52.576Z
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Daniil Gavrilov
kefirski
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kefirski
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Ruslan Rakhimov
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2026-03-05T09:59:54.556Z
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Ruslan Rakhimov
rusrakhimov
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rusrakhimov
Next Embedding Prediction Makes World Models Stronger
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 uses a temporal transformer to predict next-step encoder embeddings for model-based reinforcement learning without requiring decoders or auxiliary supervision.
temporal transformer next-step encoder embeddings temporal predictive alignment representation space model-based reinforcement learning DeepMind Control Suite DMLab coherent state representations predictive state representations