Offline Reinforcement Learning with Implicit Q-Learning

Last update: Dec 31, 2022

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Overview

Offline Reinforcement Learning with Implicit Q-Learning

This repository contains the official implementation of Offline Reinforcement Learning with Implicit Q-Learning by Ilya Kostrikov, Ashvin Nair, and Sergey Levine.

If you use this code for your research, please consider citing the paper:

@article{kostrikov2021iql,
    title={Offline Reinforcement Learning with Implicit Q-Learning},
    author={Ilya Kostrikov and Ashvin Nair and Sergey Levine},
    year={2021},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

How to run the code

Install dependencies

pip install -r requirements.txt

See instructions for CUDA.

Run training

Locomotion

python train_offline.py --env_name=halfcheetah-medium-expert-v2 --config=configs/mujoco_config.py

AntMaze

python train_offline.py --env_name=antmaze-large-play-v0 --config=configs/antmaze_config.py --eval_episodes=100 --eval_interval=100000

Kitchen and Adroit

python train_offline.py --env_name=pen-human-v0 --config=configs/kitchen_config.py

Misc

The implementation is based on JAXRL.

Offline Reinforcement Learning with Implicit Q-Learning

Related tags

Overview

Offline Reinforcement Learning with Implicit Q-Learning

How to run the code

Install dependencies

Run training

Misc

Owner

Ilya Kostrikov

[CVPR 2021] Region-aware Adaptive Instance Normalization for Image Harmonization

Used to record WKU's utility bills on a regular basis.

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SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)

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Official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution"

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Object classification with basic computer vision techniques

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Code to produce syntactic representations that can be used to study syntax processing in the human brain

Place holder for HOPE: a human-centric and task-oriented MT evaluation framework using professional post-editing

This is the code of "Multi-view Contrastive Graph Clustering" in NeurlPS 2021.

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI

From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

CrossMLP - The repository offers the official implementation of our BMVC 2021 paper (oral) in PyTorch.

Video lie detector using xgboost - A video lie detector using OpenFace and xgboost

Crawl & visualize ICLR papers and reviews

Specificity-preserving RGB-D Saliency Detection

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Offline Reinforcement Learning with Implicit Q-Learning

Related tags

Overview

Offline Reinforcement Learning with Implicit Q-Learning

How to run the code

Install dependencies

Run training

Misc

Owner

Ilya Kostrikov

[CVPR 2021] Region-aware Adaptive Instance Normalization for Image Harmonization

Used to record WKU's utility bills on a regular basis.

Answering Open-Domain Questions of Varying Reasoning Steps from Text

SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)

[CVPR 2021] Teachers Do More Than Teach: Compressing Image-to-Image Models (CAT)

Official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution"

Deeper insights into graph convolutional networks for semi-supervised learning

GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

Object classification with basic computer vision techniques

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Code to produce syntactic representations that can be used to study syntax processing in the human brain

Place holder for HOPE: a human-centric and task-oriented MT evaluation framework using professional post-editing

This is the code of "Multi-view Contrastive Graph Clustering" in NeurlPS 2021.

This is the pytorch implementation for the paper: *Learning Accurate Performance Predictors for Ultrafast Automated Model Compression*, which is in submission to TPAMI

From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

CrossMLP - The repository offers the official implementation of our BMVC 2021 paper (oral) in PyTorch.

Video lie detector using xgboost - A video lie detector using OpenFace and xgboost

Crawl & visualize ICLR papers and reviews

Specificity-preserving RGB-D Saliency Detection

Tools for manipulating UVs in the Blender viewport.

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI