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Inverse reinforcement learning experiments from variational discriminator bottleneck (VDB) paper (https://openreview.net/pdf?id=HyxPx3R9tm)
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analysis
inverse_rl
multiple_irl
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README.md
setup.py

README.md

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.
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