Code for NeurIPS 2021 paper: Invariant Causal Imitation Learning for Generalizable Policies

Overview

Invariant Causal Imitation Learning for Generalizable Policies

Ioana Bica, Daniel Jarrett, Mihaela van der Schaar

Neural Information Processing Systems (NeurIPS) 2021

Dependencies

The code was implemented in Python 3.6 and the following packages are needed for running it:

  • gym==0.17.2

  • numpy==1.18.2

  • pandas==1.0.4

  • tensorflow==1.15.0

  • torch==1.6.0

  • tqdm==4.32.1

  • scipy==1.1.0

  • scikit-learn==0.22.2

  • stable-baselines==2.10.1

Running and evaluating the model:

The control tasks used for experiments are from OpenAI gym [1]. Each control task is associated with a true reward function (unknown to the imitation algorithm). In each case, the “expert” demonstrator can be obtained by using a pre-trained and hyperparameter-optimized agent from the RL Baselines Zoo [2] in Stable OpenAI Baselines [3].

In this implementation we provide the expert demonstrations for 2 environments for CartPole-v1 in 'volume/CartPole-v1'. Note that the code in 'contrib/baselines_zoo' was taken from [2].

To train and evaluate ICIL on CartPole-v1, run the following command with the chosen command line arguments. For reference, the expert performance is 500.

python testing/il.py
Options :
   --env                  # Environment name. 
   --num_trajectories	  # Number of expert trajectories used for training the imitation learning algorithm. 
   --trial                # Trial number.

Outputs:

  • Average reward for 10 repetitions of running ICIL.

Example usage

python testing/il.py  --env='CartPole-v1' --num_trajectories=20 --trial=0 

References

[1] Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym. OpenAI, 2016

[2] Antonin Raffin. Rl baselines zoo. https://github.com/araffin/rl-baselines-zoo, 2018

[3] Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu. Stable baselines. https://github.com/hill-a/stable-baselines, 2018.

Citation

If you use this code, please cite:

@inproceedings{bica2021invariant,
  title={Invariant Causal Imitation Learning for Generalizable Policies},
  author={Bica, Ioana and Jarrett, Daniel and van der Schaar, Mihaela},
  booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
  year={2021}
}
Owner
Ioana Bica
Ioana Bica
Easy genetic ancestry predictions in Python

ezancestry Easily visualize your direct-to-consumer genetics next to 2500+ samples from the 1000 genomes project. Evaluate the performance of a custom

Kevin Arvai 38 Jan 02, 2023
Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters.

Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters. Overview This project is a Torch implementation for our CVPR 2016 paper

Jianwei Yang 278 Dec 25, 2022
CARL provides highly configurable contextual extensions to several well-known RL environments.

CARL (context adaptive RL) provides highly configurable contextual extensions to several well-known RL environments.

AutoML-Freiburg-Hannover 51 Dec 28, 2022
PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"

Adam-NSCL This is a PyTorch implementation of Adam-NSCL algorithm for continual learning from our CVPR2021 (oral) paper: Title: Training Networks in N

Shipeng Wang 34 Dec 21, 2022
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

Hannes Stärk 355 Jan 03, 2023
Credo AI Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data assessment, and acts as a central gateway to assessments created in the open source community.

Lens by Credo AI - Responsible AI Assessment Framework Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data a

Credo AI 27 Dec 14, 2022
Improving Non-autoregressive Generation with Mixup Training

MIST Training MIST TRAIN_FILE=/your/path/to/train.json VALID_FILE=/your/path/to/valid.json OUTPUT_DIR=/your/path/to/save_checkpoints CACHE_DIR=/your/p

7 Nov 22, 2022
CLOOB training (JAX) and inference (JAX and PyTorch)

cloob-training Pretrained models There are two pretrained CLOOB models in this repo at the moment, a 16 epoch and a 32 epoch ViT-B/16 checkpoint train

Katherine Crowson 64 Nov 27, 2022
Implementation of the paper Recurrent Glimpse-based Decoder for Detection with Transformer.

REGO-Deformable DETR By Zhe Chen, Jing Zhang, and Dacheng Tao. This repository is the implementation of the paper Recurrent Glimpse-based Decoder for

Zhe Chen 33 Nov 30, 2022
4st place solution for the PBVS 2022 Multi-modal Aerial View Object Classification Challenge - Track 1 (SAR) at PBVS2022

A Two-Stage Shake-Shake Network for Long-tailed Recognition of SAR Aerial View Objects 4st place solution for the PBVS 2022 Multi-modal Aerial View Ob

LinpengPan 5 Nov 09, 2022
UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

UnivNet UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation. Training python train.py --c

Rishikesh (ऋषिकेश) 55 Dec 26, 2022
Data for "Driving the Herd: Search Engines as Content Influencers" paper

herding_data Data for "Driving the Herd: Search Engines as Content Influencers" paper Dataset description The collection contains 2250 documents, 30 i

0 Aug 17, 2021
Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

1 Jan 16, 2022
End-to-End Object Detection with Fully Convolutional Network

This project provides an implementation for "End-to-End Object Detection with Fully Convolutional Network" on PyTorch.

472 Dec 22, 2022
Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Christopher T. Chubb 35 Dec 21, 2022
The-Secret-Sharing-Schemes - This interactive script demonstrates the Secret Sharing Schemes algorithm

The-Secret-Sharing-Schemes This interactive script demonstrates the Secret Shari

Nishaant Goswamy 1 Jan 02, 2022
Differentiable Wavetable Synthesis

Differentiable Wavetable Synthesis

4 Feb 11, 2022
Pytorch implementation of MLP-Mixer with loading pre-trained models.

MLP-Mixer-Pytorch PyTorch implementation of MLP-Mixer: An all-MLP Architecture for Vision with the function of loading official ImageNet pre-trained p

Qiushi Yang 2 Sep 29, 2022
Implementation of PyTorch-based multi-task pre-trained models

mtdp Library containing implementation related to the research paper "Multi-task pre-training of deep neural networks for digital pathology" (Mormont

Romain Mormont 27 Oct 14, 2022
This repository for project that can Automate Number Plate Recognition (ANPR) in Morocco Licensed Vehicles. 💻 + 🚙 + 🇲🇦 = 🤖 🕵🏻‍♂️

MoroccoAI Data Challenge (Edition #001) This Reposotory is result of our work in the comepetiton organized by MoroccoAI in the context of the first Mo

SAFOINE EL KHABICH 14 Oct 31, 2022