AdamStelmaszczyk/learning2run
Our NIPS 2017: Learning to Run source code
This project provides the core code for training virtual musculoskeletal models to run in simulated environments. It takes in a model of a human or animal with joints and muscles, applies various reinforcement learning algorithms, and outputs a trained policy that enables the model to move autonomously and efficiently. This is primarily for biomechanical researchers and robotics engineers exploring advanced locomotion and control systems.
Use this if you are a researcher in biomechanics or robotics looking to experiment with different reinforcement learning approaches to teach simulated agents complex motor skills like running.
Not ideal if you need an out-of-the-box solution for controlling real-world robots or analyzing existing human motion data without simulation.
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Language
Python
License
MIT
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Last pushed
Mar 09, 2026
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