This is the official code base of the paper LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning, which was presented at the eleventh International Conference on Learning Representations (ICLR 2023) in Kigali Ruanda. Here, we also provide all the baselines for the LocoMuJoCo imitation learning benchmark LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion presented at the Robot Learning workshop at NeurIPS 2023.

Within this work, we analyze the effect of a squared norm regularizer on the implicit reward function in the inverse reinforcement learning setting. We build on previous work (IQ-Learn), and show that this regularizer results in a minimzation of the Chi^2-Divergence between the expert and a mixture distribution. We show that - unlike previously used divergences - this divergence is bounded and the resulting reward function is also bounded. An example is given in the picture above, where the target distribution is blue, the current policy distribution is green, and the mixture is orange. As can be seen, the vanilla Chi^2 divergence can reach very high values - despite the support area being non-zero - while the divergence on the mixture is bounded. Both optimization share the same optimal solution.

Also, this regularizer provides a particularly illuminating perspective: the original objective can be understood as squared Bellman error minimization with fixed rewards for the expert and the policy. This setting can be further used to stabilize training as shown in our paper.

✅ Simple implementation on top of SAC
✅ Bounded objective with bounded reward yields stable and convenient training
✅ Retains performance even without expert actions
✅ Performs even when only 1 expert trajectory is given
✅ Works in complex and realistic environments such as on the Atlas Locomotion task
✅ Unlike previous methods, no survival bias!

You can install this repo by cloning and then

cd ls-iq
pip install -e .

In order to run the examples and reproduce the results, you have to download the datasets used in our paper. To do so, you have to install gdown:

pip install gdown

Then you can just run the download script:

chmod u+x ./download_data.sh
./download_data.sh

You can find launcher files in the example folder to launch all different versions of LSIQ and to reproduce the main results of the paper.

Here is how you run the training of LSIQ with 5 expert trajectories on all Mujoco Gym Tasks:

cd examples/02_episode_5/
python launcher.py

To monitor the training, you have to use Tensorboard. Once the training is launched, the directory logs will be created, which contains the Tensorboard logging data. Here is how you run Tensorboard:

tensorboard --logdir logs

Some experiments were such as the Atlas locomotion task were conducted on environment, which are yet not available on Mushroom-RL, but will be available soon! Once the environments are part of Mushroom-RL, the experiment files will be added here. Follow Mushroom-RL on Twitter @Mushroom_RL to immediately get notified once the new environment package is available!

@inproceedings{alhafez2023,
title={LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning},
author={Firas Al-Hafez and Davide Tateo and Oleg Arenz and Guoping Zhao and Jan Peters},
booktitle={Eleventh International Conference on Learning Representations (ICLR)},
year={2023},
url={https://openreview.net/pdf?id=o3Q4m8jg4BR}}