Probabilistic Dreaming for World Models
#probabilistic dreaming #world models #AI #reinforcement learning #uncertainty #simulation #decision-making
📌 Key Takeaways
- Probabilistic dreaming enhances world models by simulating diverse future scenarios.
- It improves AI's ability to predict and adapt to uncertain environments.
- The method integrates probabilistic reasoning into model-based reinforcement learning.
- This approach can lead to more robust and efficient AI decision-making.
📖 Full Retelling
arXiv:2603.04715v1 Announce Type: cross
Abstract: "Dreaming" enables agents to learn from imagined experiences, enabling more robust and sample-efficient learning of world models. In this work, we consider innovations to the state-of-the-art Dreamer model using probabilistic methods that enable: (1) the parallel exploration of many latent states; and (2) maintaining distinct hypotheses for mutually exclusive futures while retaining the desirable gradient properties of continuous latents. Evalua
🏷️ Themes
AI Research, Machine Learning
📚 Related People & Topics
Artificial intelligence
Intelligence of machines
# Artificial Intelligence (AI) **Artificial Intelligence (AI)** is a specialized field of computer science dedicated to the development and study of computational systems capable of performing tasks typically associated with human intelligence. These tasks include learning, reasoning, problem-solvi...
Entity Intersection Graph
Connections for Artificial intelligence:
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OpenAI
14 shared
🌐
Reinforcement learning
4 shared
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Anthropic
4 shared
🌐
Large language model
3 shared
🏢
Nvidia
3 shared
Mentioned Entities
Original Source
--> Computer Science > Machine Learning arXiv:2603.04715 [Submitted on 5 Mar 2026] Title: Probabilistic Dreaming for World Models Authors: Gavin Wong View a PDF of the paper titled Probabilistic Dreaming for World Models, by Gavin Wong View PDF HTML Abstract: "Dreaming" enables agents to learn from imagined experiences, enabling more robust and sample-efficient learning of world models. In this work, we consider innovations to the state-of-the-art Dreamer model using probabilistic methods that enable: (1) the parallel exploration of many latent states 2) maintaining distinct hypotheses for mutually exclusive futures while retaining the desirable gradient properties of continuous latents. Evaluating on the MPE SimpleTag domain, our method outperforms standard Dreamer with a 4.5% score improvement and 28% lower variance in episode returns. We also discuss limitations and directions for future work, including how optimal hyperparameters (e.g. particle count K) scale with environmental complexity, and methods to capture epistemic uncertainty in world models. Comments: Presented at ICLR 2026: 2nd Workshop on World Models Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04715 [cs.LG] (or arXiv:2603.04715v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.04715 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Gavin Wong [ view email ] [v1] Thu, 5 Mar 2026 01:32:40 UTC (37 KB) Full-text links: Access Paper: View a PDF of the paper titled Probabilistic Dreaming for World Models, by Gavin Wong View PDF HTML TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibli...
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