danijar/dreamerv2
Mastering Atari with Discrete World Models
This project helps reinforcement learning researchers and practitioners train agents that can master complex tasks, particularly in simulated environments like Atari games or robotic control. You provide the environment's visual observations, and it outputs a highly skilled agent capable of achieving human-level or better performance. It's designed for those developing or evaluating advanced AI agents.
1,012 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to train a high-performing AI agent for simulated environments by learning directly from pixel inputs.
Not ideal if your primary goal is to deploy an agent in a real-world physical system without significant adaptation, or if you need a simple, interpretable model for basic tasks.
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1,012
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210
Language
Python
License
MIT
Category
Last pushed
Jan 21, 2023
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