World Models with DreamerV3 - AI Build & Learn

Welcome to AI Build & Learn, a weekly AI engineering stream where we pick a new topic and learn by building together.

​This event kicks off a run on world models: models that learn an internal representation of how an environment works, then use it to predict what happens next and to plan. It's a natural next step after the generation and RL events, tying both threads together.

​We're starting with DreamerV3, a model-based RL agent and a great on-ramp to the idea. Dreamer learns a compact world model of its environment from experience, then trains its policy almost entirely inside imagined rollouts of that model rather than the real environment. It's lightweight (trains on a single GPU), works across many tasks with the same settings, and connects directly to the RL and MuJoCo work.

​Some things to look up to get started:
​Model:

• ​DreamerV3 (Danijar Hafner): https://github.com/danijar/dreamerv3
• ​Paper: "Mastering Diverse Domains through World Models" https://arxiv.org/abs/2301.04104

​Background:

• ​The original "World Models" paper (Ha and Schmidhuber): https://worldmodels.github.io/

​​​Resources

• ​​​GitHub: https://github.com/sagecodes/ai-build-and-learn
• ​​​Events Calendar: https://luma.com/ai-builders-and-learners
• ​​​Slack (Discuss during the week): https://slack.flyte.org/
• ​​​Hosted by Sage Elliott: https://www.linkedin.com/in/sageelliott/

​​In this stream

• Intro to topic
• ​​​​Community Discussion
• Practical examples

​​​Community challenge (optional)
​​​Try spending 30–90 minutes during the week learning or building something related to the topic, then share what you’re working on in Slack.

​​​Note on Flyte / Union
​​​You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.

• ​​​Union: https://www.union.ai/
• ​​​Flyte: https://flyte.org/

​​​Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).