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).
- 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
expert_policies/*are the expert policies, trained by TRPO (
scripts/run_rl_mj.py) on the true costs
scripts/im_pipeline.pyis 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
results/*contain evaluation data for the learned policies