VDB IRL experiments

Implementation of Adversarial IRL (AIRL) with information bottleneck. Used in the Variational Discriminator Bottleneck (VDB) paper at ICLR.

Getting Set Up

• Install rllab if not already. Try qxcv/rllab on the minor-fixes branch (which adds some missing hooks & addresses some bugs in the original RLLab).
• Add folders multiple_irl, inverse_rl, and scripts to python path (to double check that this works, just try importing multiple_irl.envs from a python shell). The easiest way to do this is using the setup.py file in this directory, with pip install -e ..

When running scripts, you ought to run then directly from the root folder of the git repository.

Developing

The core algorithm is in `multiple_irl/models/shaped_irl.py.

All the environments are in multiple_irl/envs: you can also find a comprehensive list of environments below in the README.

All the scripts are in scripts/scripts_generic. The scripts do the following:

• data_collect.py: This script collects expert trajectories.
• env_compare.py: This script trains an AIRL reward function on a single task.
• env_retrain.py: Takes a trained AIRL reward function and uses it to train a new policy from scratch in an environment.