GPS

This code is a reimplementation of the guided policy search algorithm and LQG-based trajectory optimization, meant to help others understand, reuse, and build upon existing work.

For full documentation, see rll.berkeley.edu/gps.

The code base is a work in progress. See the FAQ for information on planned future additions to the code.

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This version adds following features:

• Better TensorFlow support
• Add experiments for training agent_box2d to reach any goal position from any starting position
• Create a UR agent which has stable action publish frequency
• Add support for multithreading sampling for UR agent
• Replace the original GMM with GaussianMixture from sklearn
• Add experiments which can train UR robot to go to any target position
• Add experiments which can train UR robot to go to any target poisition with specified orientation
• Add AlgorithmSL to support training agent with pure supervised learning without any optimal control, to demonstrate the necessity of optimal control

To see the official code, please visit https://github.com/cbfinn/gps