danaugrs/huskarl
Deep Reinforcement Learning Framework + Algorithms
Huskarl is for machine learning engineers and researchers who are developing AI agents that learn by interacting with an environment, similar to how a human learns through trial and error. It helps build and experiment with deep reinforcement learning algorithms, taking in environment observations and outputting an agent capable of making optimal decisions. It's used by those creating intelligent systems for tasks like game playing, robotics, or autonomous control.
415 stars. No commits in the last 6 months. Available on PyPI.
Use this if you are a machine learning engineer building intelligent agents that need to learn optimal strategies through reinforcement from interactions within a simulated or real-world environment.
Not ideal if you are looking for a tool for traditional supervised or unsupervised machine learning tasks like classification, regression, or clustering.
Stars
415
Forks
51
Language
Python
License
MIT
Category
Last pushed
Mar 25, 2023
Commits (30d)
0
Dependencies
3
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