/imitation

Contains an implementation of "Trust Region Policy Optimization" (TRPO)

Primary LanguagePythonMIT LicenseMIT

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