DeepMind Alchemy task environment: a meta-reinforcement learning benchmark

Overview

dm_alchemy: DeepMind Alchemy environment

Overview | Requirements | Installation | Usage | Documentation | Tutorial | Paper | Blog post

The DeepMind Alchemy environment is a meta-reinforcement learning benchmark that presents tasks sampled from a task distribution with deep underlying structure. It was created to test for the ability of agents to reason and plan via latent state inference, as well as useful exploration and experimentation. It is Unity-based.

Overview

This environment is provided through pre-packaged Docker containers.

This package consists of support code to run these Docker containers. You interact with the task environment via a dm_env Python interface.

Please see the documentation for more detailed information on the available tasks, actions and observations.

Requirements

dm_alchemy requires Docker, Python 3.6.1 or later and a x86-64 CPU with SSE4.2 support. We do not attempt to maintain a working version for Python 2.

Alchemy is intended to be run on Linux and is not officially supported on Mac and Windows. However, it can in principle be run on any platform (though installation may be more of a headache). In particular, on Windows, you will need to install and run Alchemy with WSL.

Note: We recommend using Python virtual environment to mitigate conflicts with your system's Python environment.

Download and install Docker:

Ensure that docker is working correctly by running docker run -d gcr.io/deepmind-environments/alchemy:v1.0.0.

Installation

You can install dm_alchemy by cloning a local copy of our GitHub repository:

$ git clone https://github.com/deepmind/dm_alchemy.git
$ pip install wheel
$ pip install --upgrade setuptools
$ pip install ./dm_alchemy

To also install the dependencies for the examples/, install with:

$ pip install ./dm_alchemy[examples]

Usage

Once dm_alchemy is installed, to instantiate a dm_env instance run the following:

import dm_alchemy

LEVEL_NAME = ('alchemy/perceptual_mapping_'
              'randomized_with_rotation_and_random_bottleneck')
settings = dm_alchemy.EnvironmentSettings(seed=123, level_name=LEVEL_NAME)
env = dm_alchemy.load_from_docker(settings)

For more details see the introductory colab.

Open in colab

Citing Alchemy

If you use Alchemy in your work, please cite the accompanying technical report:

@article{wang2021alchemy,
    title={Alchemy: A structured task distribution for meta-reinforcement learning},
    author={Jane Wang and Michael King and Nicolas Porcel and Zeb Kurth-Nelson
        and Tina Zhu and Charlie Deck and Peter Choy and Mary Cassin and
        Malcolm Reynolds and Francis Song and Gavin Buttimore and David Reichert
        and Neil Rabinowitz and Loic Matthey and Demis Hassabis and Alex Lerchner
        and Matthew Botvinick},
    year={2021},
    journal={arXiv preprint arXiv:2102.02926},
    url={https://arxiv.org/abs/2102.02926},
}

Notice

This is not an officially supported Google product.

Owner
DeepMind
DeepMind
Source code for paper "Deep Diffusion Models for Robust Channel Estimation", TBA.

diffusion-channels Source code for paper "Deep Diffusion Models for Robust Channel Estimation". Generic flow: Use 'matlab/main.mat' to generate traini

The University of Texas Computational Sensing and Imaging Lab 15 Dec 22, 2022
"Graph Neural Controlled Differential Equations for Traffic Forecasting", AAAI 2022

Graph Neural Controlled Differential Equations for Traffic Forecasting Setup Python environment for STG-NCDE Install python environment $ conda env cr

Jeongwhan Choi 55 Dec 28, 2022
🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools

Hugging Face Optimum 🤗 Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to t

Hugging Face 842 Dec 30, 2022
Tutorial materials for Part of NSU Intro to Deep Learning with PyTorch.

Intro to Deep Learning Materials are part of North South University (NSU) Intro to Deep Learning with PyTorch workshop series. (Slides) Related materi

Hasib Zunair 9 Jun 08, 2022
Learned model to estimate number of distinct values (NDV) of a population using a small sample.

Learned NDV estimator Learned model to estimate number of distinct values (NDV) of a population using a small sample. The model approximates the maxim

2 Nov 21, 2022
A best practice for tensorflow project template architecture.

A best practice for tensorflow project template architecture.

Mahmoud Gamal Salem 3.6k Dec 22, 2022
Code used to generate the results appearing in "Train longer, generalize better: closing the generalization gap in large batch training of neural networks"

Train longer, generalize better - Big batch training This is a code repository used to generate the results appearing in "Train longer, generalize bet

Elad Hoffer 145 Sep 16, 2022
bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

Code Submission for: Bio-inspired Min-Nets Improve the Performance and Robustness of Deep Networks Run with docker To build a docker environment, chan

0 Dec 09, 2021
Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks"

HKD Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks" cifia-100 result The implementation of compared methods are ba

Wang Yucheng 30 Dec 18, 2022
Convnet transfer - Code for paper How transferable are features in deep neural networks?

How transferable are features in deep neural networks? This repository contains source code necessary to reproduce the results presented in the follow

Jason Yosinski 143 Sep 13, 2022
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

Junction Tree Variational Autoencoder for Molecular Graph Generation Official implementation of our Junction Tree Variational Autoencoder https://arxi

Wengong Jin 418 Jan 07, 2023
RSNA Intracranial Hemorrhage Detection with python

RSNA Intracranial Hemorrhage Detection This is the source code for the first place solution to the RSNA2019 Intracranial Hemorrhage Detection Challeng

24 Nov 30, 2022
VOneNet: CNNs with a Primary Visual Cortex Front-End

VOneNet: CNNs with a Primary Visual Cortex Front-End A family of biologically-inspired Convolutional Neural Networks (CNNs). VOneNets have the followi

The DiCarlo Lab at MIT 99 Dec 22, 2022
Kohei's 5th place solution for xview3 challenge

xview3-kohei-solution Usage This repository assumes that the given data set is stored in the following locations: $ ls data/input/xview3/*.csv data/in

Kohei Ozaki 2 Jan 17, 2022
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

268 Jan 09, 2023
Open-Ended Commonsense Reasoning (NAACL 2021)

Open-Ended Commonsense Reasoning Quick links: [Paper] | [Video] | [Slides] | [Documentation] This is the repository of the paper, Differentiable Open-

(Bill) Yuchen Lin 31 Oct 19, 2022
Perform Linear Classification with Multi-way Data

MultiwayClassification This is an R package to perform linear classification for data with multi-way structure. The distance-weighted discrimination (

Eric F. Lock 2 Dec 15, 2020
Self-Supervised Learning with Kernel Dependence Maximization

Self-Supervised Learning with Kernel Dependence Maximization This is the code for SSL-HSIC, a self-supervised learning loss proposed in the paper Self

DeepMind 29 Dec 29, 2022
Car Parking Tracker Using OpenCv

Car Parking Vacancy Tracker Using OpenCv I used basic image processing methods i

Adwait Kelkar 30 Dec 03, 2022
Finite difference solution of 2D Poisson equation. Can handle Dirichlet, Neumann and mixed boundary conditions.

Poisson-solver-2D Finite difference solution of 2D Poisson equation Current version can handle Dirichlet, Neumann, and mixed (combination of Dirichlet

Mohammad Asif Zaman 34 Dec 23, 2022