rail-berkeley/softlearning
A deep reinforcement learning toolbox for training maximum entropy policies in continuous domains.
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Softlearning is a reinforcement learning framework that implements the Soft Actor-Critic algorithm for training maximum entropy policies. It uses TensorFlow’s tf.keras modules for model classes like policies and value functions, and leverages Ray Tune for orchestrating and distributing experiments across cloud services. The framework targets continuous control tasks and integrates with MuJoCo for physics simulation.
Frequently asked
- What is rail-berkeley/softlearning?
- A deep reinforcement learning toolbox for training maximum entropy policies in continuous domains.
- Is softlearning open source?
- Yes — rail-berkeley/softlearning is an open-source project tracked on heatdrop.
- What language is softlearning written in?
- rail-berkeley/softlearning is primarily written in Python.
- How popular is softlearning?
- rail-berkeley/softlearning has 1.4k stars on GitHub.
- Where can I find softlearning?
- rail-berkeley/softlearning is on GitHub at https://github.com/rail-berkeley/softlearning.