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Status: Archive (code is provided as-is, no updates expected)

Generative Adversarial Imitation Learning

Jonathan Ho and Stefano Ermon

Contains an implementation of Trust Region Policy Optimization (Schulman et al., 2015).

Dependencies:

  • OpenAI Gym >= 0.1.0, mujoco_py >= 0.4.0
  • numpy >= 1.10.4, scipy >= 0.17.0, theano >= 0.8.2
  • h5py, pytables, pandas, matplotlib

Provided files:

  • expert_policies/* are the expert policies, trained by TRPO (scripts/run_rl_mj.py) on the true costs
  • scripts/im_pipeline.py is the main training and evaluation pipeline. This script is responsible for sampling data from experts to generate training data, running the training code (scripts/imitate_mj.py), and evaluating the resulting policies.
  • pipelines/* are the experiment specifications provided to scripts/im_pipeline.py
  • results/* contain evaluation data for the learned policies

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Code for the paper "Generative Adversarial Imitation Learning"

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