Just Another DAgger Implementation
DAgger is a reinforcement learning (imitation learning, to be exact) algorithm that uses data aggregation techniques to address the states distribution mismatch problem. The detailed algorithm is described in the paper.
This is my implementation of the DAgger. The code is based on the starter code and policy function generously provided by the Berkeley CS294 course.
In the Humanoid experiment, the goal is to train the humanoid to walk fast forward without falling. Below is the humanoid trained using DAgger.
How to run the code
Run the following command:
(More coming soon...)