OREO: Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning (NeurIPS 2021)

Related tags

Deep Learningoreo
Overview

OREO: Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning (NeurIPS 2021)

Video demo

We here provide a video demo from confounded Enduro environment (see Figure 8 of the main draft). We also visualize the spatial attention map from a convolutional encoder trained with BC (medium) and OREO (right).

Enduro_total_demo_cropped

Installation

OREO requires CUDA 10.1 to run.

Install the dependencies:

conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch
pip install dopamine_rl sklearn tqdm kornia dropblock atari-py==0.2.6 gsutil

Download DQN Replay dataset for expert demonstrations on Atari environments:

mkdir DATAPATH
cp download.sh DATAPATH
cd DATAPATH
sh download.sh

Pre-training

We here provide beta-VAE (for CCIL) and VQ-VAE (for CRLR and OREO) pretraining scripts. For other datasets, change the --env option.

beta-VAE

CUDA_VISIBLE_DEVICES=0,1,2,3 python atari_beta_vae.py --env=KungFuMaster --datapath DATAPATH --num_episodes 20 --seed 1 --ch_div 4 --lmd 10

VQ-VAE

CUDA_VISIBLE_DEVICES=0,1,2,3 python atari_vqvae.py --env=KungFuMaster --datapath DATAPATH --num_episodes 20 --seed 1

Training BC policy

We here provide training scripts for baselines and OREO. For other datasets, change the --env, --beta_vae_path, and --vqvae_path options.

Behavioral cloning

CUDA_VISIBLE_DEVICES=0 python atari_cnn_actor.py --env=KungFuMaster --datapath DATAPATH --seed 1 --eval_interval 1000 --num_episodes 20 --num_eval_episodes 100

Dropout

CUDA_VISIBLE_DEVICES=0 python atari_cnn_actor.py --env=KungFuMaster --datapath DATAPATH --seed 1 --eval_interval 1000 --original_dropout --prob 0.5 --num_episodes 20 --num_eval_episodes 100

DropBlock

CUDA_VISIBLE_DEVICES=0 python atari_cnn_actor.py --env=KungFuMaster --datapath DATAPATH --seed 1 --eval_interval 1000 --dropblock --prob 0.3 --num_episodes 20 --num_eval_episodes 100

Cutout

CUDA_VISIBLE_DEVICES=0 python atari_cnn_actor.py --env=KungFuMaster --datapath DATAPATH --seed 1 --eval_interval 1000 --input_cutout --num_episodes 20 --num_eval_episodes 100

RandomShift

CUDA_VISIBLE_DEVICES=0 python atari_cnn_actor.py --env=KungFuMaster --datapath DATAPATH --seed 1 --eval_interval 1000 --random_shift --num_episodes 20 --num_eval_episodes 100

CCIL (w/o interaction)

CUDA_VISIBLE_DEVICES=0 python atari_beta_vae_actor.py --env=KungFuMaster --datapath DATAPATH --num_episodes 20 --num_eval_episodes 100 --seed 1 --eval_interval 1000 --prob 0.5 --ch_div 4 --beta_vae_path models_beta_vae_coord_conv_chdiv4_actor_lmd10.0/KungFuMaster_s1_epi20_con1_seed1_zdim50_beta4_kltol0_ep1000_beta_vae.pth

CRLR

CUDA_VISIBLE_DEVICES=0 python atari_cnn_actor_crlr.py --fixed_size 15000 --num_sub_iters 10 --eval_interval 10 --save_interval 10 --n_epochs 10 --env=KungFuMaster --datapath DATAPATH --num_episodes 20 --num_eval_episodes 100 --seed 1 --vqvae_path models_vqvae/KungFuMaster_s1_epi20_con1_seed1_ne512_c0.25_ep1000_vqvae.pth

OREO

CUDA_VISIBLE_DEVICES=0 python atari_vqvae_oreo.py --env=KungFuMaster --datapath DATAPATH --num_mask 5 --num_episodes 20 --num_eval_episodes 100 --seed 1 --eval_interval 1000 --prob 0.5 --vqvae_path models_vqvae/KungFuMaster_s1_epi20_con1_seed1_ne512_c0.25_ep1000_vqvae.pth
Owner
GitHub Repository
Large scale embeddings on a single machine.

Marius Marius is a system under active development for training embeddings for large-scale graphs on a single machine. Training on large scale graphs

Marius 107 Jan 03, 2023
TC-GNN with Pytorch integration

TC-GNN (Running Sparse GNN on Dense Tensor Core on Ampere GPU) Cite this project and paper. @inproceedings{TC-GNN, title={TC-GNN: Accelerating Spars

YUKE WANG 19 Dec 01, 2022
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

DistMIS Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation. DistriMIS Distributing Deep Learning Hyperparameter Tuning

HiEST 2 Sep 09, 2022
TensorFlow (Python) implementation of DeepTCN model for multivariate time series forecasting.

DeepTCN TensorFlow TensorFlow (Python) implementation of multivariate time series forecasting model introduced in Chen, Y., Kang, Y., Chen, Y., & Wang

Flavia Giammarino 21 Dec 19, 2022
This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022

CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC

LEAP Lab 2 Sep 15, 2022
Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"

Dangers of Bayesian Model Averaging under Covariate Shift This repository contains the code to reproduce the experiments in the paper Dangers of Bayes

Pavel Izmailov 25 Sep 21, 2022
discovering subdomains, hidden paths, extracting unique links

python-website-crawler discovering subdomains, hidden paths, extracting unique links pip install -r requirements.txt discover subdomain: You can give

merve 4 Sep 05, 2022
Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.

Monk - A computer vision toolkit for everyone Why use Monk Issue: Want to begin learning computer vision Solution: Start with Monk's hands-on study ro

Tessellate Imaging 507 Dec 04, 2022
Official Tensorflow implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"

M-LSD: Towards Light-weight and Real-time Line Segment Detection Official Tensorflow implementation of "M-LSD: Towards Light-weight and Real-time Line

NAVER/LINE Vision 357 Jan 04, 2023
Pytorch implementation for "Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter".

Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter This is a pytorch-based implementation for paper Implicit Feature Alignme

wangtianwei 61 Nov 12, 2022
Convert Table data to approximate values with GUI

Table_Editor Convert Table data to approximate values with GUIs... usage - Import methods for extension Tables. Imported method supposed to have only

CLJ 1 Jan 10, 2022
This project provides an unsupervised framework for mining and tagging quality phrases on text corpora with pretrained language models (KDD'21).

UCPhrase: Unsupervised Context-aware Quality Phrase Tagging To appear on KDD'21...[pdf] This project provides an unsupervised framework for mining and

Xiaotao Gu 146 Dec 22, 2022
PyTorch code for: Learning to Generate Grounded Visual Captions without Localization Supervision

Learning to Generate Grounded Visual Captions without Localization Supervision This is the PyTorch implementation of our paper: Learning to Generate G

Chih-Yao Ma 41 Nov 17, 2022
Official codebase for ICLR oral paper Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling

CLIORA This is the official codebase for ICLR oral paper: Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling. We introduce

Bo Wan 32 Dec 23, 2022
Official PyTorch implementation for paper Context Matters: Graph-based Self-supervised Representation Learning for Medical Images

Context Matters: Graph-based Self-supervised Representation Learning for Medical Images Official PyTorch implementation for paper Context Matters: Gra

49 Nov 23, 2022
Generate image analogies using neural matching and blending

neural image analogies This is basically an implementation of this "Image Analogies" paper, In our case, we use feature maps from VGG16. The patch mat

Adam Wentz 3.5k Jan 08, 2023
Implementation for our AAAI2021 paper (Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction).

SSAN Introduction This is the pytorch implementation of the SSAN model (see our AAAI2021 paper: Entity Structure Within and Throughout: Modeling Menti

benfeng 69 Nov 15, 2022
VOGUE: Try-On by StyleGAN Interpolation Optimization

VOGUE is a StyleGAN interpolation optimization algorithm for photo-realistic try-on. Top: shirt try-on automatically synthesized by our method in two different examples.

Wei ZHANG 66 Dec 09, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Jan 08, 2023
Algebraic effect handlers in Python

PyEffect: Algebraic effects in Python What IDK. Usage effects.handle(operation, handlers=None) effects.set_handler(effect, handler) Supported effects

Greg Werbin 5 Dec 27, 2021