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Yup, the technical details are described in this paper from early 2023. Seems like Nature takes its time. That used to be a good thing :) https://arxiv.org/abs/2301.04104
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Mastering Diverse Domains through World Models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap
2023 · arXiv
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From the abstract
Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforcement learning algorithms can be readily applied to tasks similar to what they have been developed for, configuring them for new application domains requires significant human expertise and experimentation. We present DreamerV3, a general algorithm that outperforms specialized methods across over 150 diverse tasks, with a single configuration. Dreamer learns a model of the environment and improves its behavior by imagining future scenarios. Robustness techniques based on normalization, balancing, and transformations enable stable learning across domains. Applied out of the box, Dreamer is the first algorithm to collect diamonds in Minecraft from scratch without human data or curricula. This achievement has been posed as a significant challenge in artificial intelligence that requires exploring farsighted strategies from pixels and sparse rewards in an open world. Our work allows solving challenging control problems without extensive experimentation, making reinforcement learning broadly applicable.
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Neutral mention · rule
AI Research Residency Program (1 year) | Zug/Zurich area | Full time | On site | 100-200k CHF/annum I am launching a new AI research organisation in the Zug/Zurich area. The goal of the organisation is to make a meaningful contribution towards solving AGI. I am looking for employees number no. 1 and 2. This will ideally be someone with a history of interesting research and a clear agenda for future research. I am personally interested in LLM's, MuZero (https://arxiv.org/abs/1911.08265), Dreamer (https://arxiv.org/pdf/2301.04104v1.pdf) and DreamCoder (https://arxiv.org/abs/2006.08381), and think there are some interesting systems to be found by interpolating between these…
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existing areas (however, please still apply even if your research isn't in these areas). The project will be financed by my current company which has made extensive use of deep learning for the past 4 years. You will have access to the >100 top-end GPUs owned by this company. The job will be structured as a research residency program in the first year, with the hope of transitioning successful candidates into full-time roles. In your application, please include a high-level research proposal stating what you would work on. To apply, email me at : not_a_cat at fastmail. co[m].
Neutral mention · rule
Paper provides a model based reinforcement learning (MBRL) algorithm that performs well over a wide variety of environments, from 2D to 3D, simple to complex. It can get diamonds in a simplified version of Minecraft and work well out of the box without tuning. ____ Paper: - https://arxiv.org/pdf/2301.04104v1.pdf Third-party review of paper: - https://youtube.com/watch?v=vfpZu0R1s1Y Code: - “Coming Soon”
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