denisyarats/pytorch_sac
PyTorch implementation of Soft Actor-Critic (SAC)
This is a PyTorch implementation of the Soft Actor-Critic (SAC) algorithm, designed for researchers and practitioners in reinforcement learning. It allows you to train an agent to perform tasks in simulated environments, using sensor readings as input and producing optimized actions as output. The tool is for machine learning researchers and AI developers working on agent-based systems.
591 stars. No commits in the last 6 months.
Use this if you are a researcher or AI developer working on reinforcement learning and need to implement and experiment with the Soft Actor-Critic (SAC) algorithm for training autonomous agents.
Not ideal if you are looking for a pre-trained agent or a drag-and-drop solution for a specific real-world automation problem without delving into the underlying reinforcement learning algorithms.
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591
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110
Language
Jupyter Notebook
License
MIT
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Last pushed
Dec 05, 2021
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