artem-oppermann/Deep-Reinforcement-Learning
A collection of several Deep Reinforcement Learning techniques (Deep Q Learning, Policy Gradients, ...), gets updated over time.
This project provides pre-built artificial intelligence models that can learn to perform tasks by trial and error, like balancing a pole or driving a car up a hill. It takes in descriptions of control problems and outputs trained AI agents capable of solving these problems autonomously. This is useful for researchers and practitioners in robotics, automation, and control systems who need to develop intelligent agents.
No commits in the last 6 months.
Use this if you are exploring how AI can learn to control dynamic systems through reinforcement, especially for classical control problems.
Not ideal if you need a plug-and-play solution for a real-world, complex robotics or industrial automation task without further development.
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Language
Python
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Last pushed
Jan 14, 2020
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