danijar/dreamerv3
Mastering Diverse Domains through World Models
This project offers a reinforcement learning algorithm that helps train AI agents to master a wide array of complex control tasks, from playing games to robot navigation. You provide data from various simulated or real-world interactions, and the system outputs a highly optimized policy for the agent's behavior. This is ideal for AI researchers and engineers working on autonomous systems or generalized AI.
2,917 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need a robust and scalable reinforcement learning solution that can adapt to many different control environments without extensive hyperparameter tuning.
Not ideal if you are looking for a pre-trained solution or a tool for simpler, non-control-based machine learning problems.
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484
Language
Python
License
MIT
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Last pushed
Sep 23, 2025
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