a Lightweight library for sequential learning agents, including reinforcement learning

Related tags

Deep Learningsalina
Overview

SaLinA: SaLinA - A Flexible and Simple Library for Learning Sequential Agents (including Reinforcement Learning)

TL;DR

salina is a lightweight library extending PyTorch modules for developping sequential decision models. It can be used for Reinforcement Learning (including model-based with differentiable environments, multi-agent RL, ...), but also in a supervised/unsupervised learning settings (for instance for NLP, Computer Vision, etc..).

  • It allows to write very complex sequential models (or policies) in few lines
  • It works on multiple CPUs and GPUs

Quick Start

  • Just clone the repo

Documentation

For development, set up pre-commit hooks:

  • Run pip install pre-commit
    • or conda install -c conda-forge pre-commit
    • or brew install pre-commit
  • In the top directory of the repo, run pre-commit install to set up the git hook scripts
  • Now pre-commit will run automatically on git commit!
  • Currently isort, black and blacken-docs are used, in that order

Organization of the repo

Dependencies

salina is making use of pytorch, hydra for configuring experiments, and of gym for reinforcement learning algorithms.

Note on the Logger

We provide a simple Logger that logs in both tensorboard format, but also as pickle files that can be re-read to make tables and figures. See logger. This logger can be easily replaced by any other logger.

Description

Sequential Decision Making is much more than Reinforcement learning

  • Sequential Decision Making is about interactions:
  • Interaction with data (e.g attention-models, decision tree, cascade models, active sensing, active learning, recommendation, etc….)
  • Interaction with an environment (e.g games, control)
  • Interaction with humans (e.g recommender systems, dialog systems, health systems, …)
  • Interaction with a model of the world (e.g simulation)
  • Interaction between multiple entities (e.g multi-agent RL)

What salina is

  • A sandbox for developping sequential models at scale.

  • A small (300 hundred lines) 'core' code that defines everything you will use to implement agents involved in sequential decision learning systems.

    • It is easy to understand and to use since it keeps the main principles of pytorch, just extending nn.Module to Agent that handle tthe temporal dimension.

A set of agents that can be combined (like pytorch modules) to obtain complex behaviors

  • A set of references implementations and examples in different domains Reinforcement learning, Imitation Learning, Computer Vision, ... (more to come..)

What salina is not

  • Yet another reinforcement learning framework: salina is focused on sequential decision making in general. It can be used for RL (which is our main current use-case), but also for supervised learning, attention models, multi-agent learning, planning, control, cascade models, recommender systems,...
  • A library: salina is just a small layer on top of pytorch that encourages good practices for implementing sequential models. It thus very simple to understand and to use, but very powerful.

Citing salina

Please use this bibtex if you want to cite this repository in your publications:

Link to the paper: SaLinA: Sequential Learning of Agents

    @misc{salina,
        author = {Ludovic Denoyer, Alfredo de la Fuente, Song Duong, Jean-Baptiste Gaya, Pierre-Alexandre Kamienny, Daniel H. Thompson},
        title = {SaLinA: Sequential Learning of Agents},
        year = {2021},
        publisher = {Arxiv},
        howpublished = {\url{https://gitHub.com/facebookresearch/salina}},
    }

Papers using SaLinA:

  • Learning a subspace of policies for online adaptation in Reinforcement Learning. Jean-Baptiste Gaya, Laure Soulier, Ludovic Denoyer - Arxiv

License

salina is released under the MIT license. See LICENSE for additional details about it. See also our Terms of Use and Privacy Policy.

Owner
Facebook Research
Facebook Research
Py4fi2nd - Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.

Python for Finance (2nd ed., O'Reilly) This repository provides all Python codes and Jupyter Notebooks of the book Python for Finance -- Mastering Dat

Yves Hilpisch 1k Jan 05, 2023
Viperdb - A tiny log-structured key-value database written in pure Python

ViperDB 🐍 ViperDB is a lightweight embedded key-value store written in pure Pyt

17 Oct 17, 2022
Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come

Pytorch Squeeznet Pytorch implementation of Squeezenet model as described in https://arxiv.org/abs/1602.07360 on cifar-10 Data. The definition of Sque

gaurav pathak 86 Oct 28, 2022
Intelligent Video Analytics toolkit based on different inference backends.

English | 中文 OpenIVA OpenIVA is an end-to-end intelligent video analytics development toolkit based on different inference backends, designed to help

Quantum Liu 15 Oct 27, 2022
Dataset and Code for the paper "DepthTrack: Unveiling the Power of RGBD Tracking" (ICCV2021), and "Depth-only Object Tracking" (BMVC2021)

DeT and DOT Code and datasets for "DepthTrack: Unveiling the Power of RGBD Tracking" (ICCV2021) "Depth-only Object Tracking" (BMVC2021) @InProceedings

Yan Song 55 Dec 15, 2022
Code for the paper "MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" (Pattern Recognition 2021)

MASTER-PyTorch PyTorch reimplementation of "MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" (Pattern Recognition 2021). This projec

Wenwen Yu 255 Dec 29, 2022
Repository for reproducing `Model-Based Robust Deep Learning`

Model-Based Robust Deep Learning (MBRDL) In this repository, we include the code necessary for reproducing the code used in Model-Based Robust Deep Le

Alex Robey 16 Sep 19, 2022
AI-Fitness-Tracker - AI Fitness Tracker With Python

AI-Fitness-Tracker We have build a AI based Fitness Tracker using OpenCV and Pyt

Sharvari Mangale 5 Feb 09, 2022
This is RFA-Toolbox, a simple and easy-to-use library that allows you to optimize your neural network architectures using receptive field analysis (RFA) and create graph visualizations of your architecture.

ReceptiveFieldAnalysisToolbox This is RFA-Toolbox, a simple and easy-to-use library that allows you to optimize your neural network architectures usin

84 Nov 23, 2022
🛠 All-in-one web-based IDE specialized for machine learning and data science.

All-in-one web-based development environment for machine learning Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issu

Machine Learning Tooling 2.9k Jan 09, 2023
Learning Skeletal Articulations with Neural Blend Shapes

This repository provides an end-to-end library for automatic character rigging and blend shapes generation as well as a visualization tool. It is based on our work Learning Skeletal Articulations wit

Peizhuo 504 Dec 30, 2022
This repository contains the code for TABS, a 3D CNN-Transformer hybrid automated brain tissue segmentation algorithm using T1w structural MRI scans

This repository contains the code for TABS, a 3D CNN-Transformer hybrid automated brain tissue segmentation algorithm using T1w structural MRI scans. TABS relies on a Res-Unet backbone, with a Vision

6 Nov 07, 2022
HandFoldingNet ✌️ : A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton

HandFoldingNet ✌️ : A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton Wencan Cheng, Jae Hyun Park, Jong

cwc1260 23 Oct 21, 2022
Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022.

Jadena Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022. arXiv

Qing Guo 13 Nov 29, 2022
EsViT: Efficient self-supervised Vision Transformers

Efficient Self-Supervised Vision Transformers (EsViT) PyTorch implementation for EsViT, built with two techniques: A multi-stage Transformer architect

Microsoft 352 Dec 25, 2022
YoHa - A practical hand tracking engine.

YoHa - A practical hand tracking engine.

2k Jan 06, 2023
CBKH: The Cornell Biomedical Knowledge Hub

Cornell Biomedical Knowledge Hub (CBKH) CBKG integrates data from 18 publicly available biomedical databases. The current version of CBKG contains a t

44 Dec 21, 2022
Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech"

GradTTS Unofficial Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech" (arxiv) About this repo This is an unoffic

HeyangXue1997 103 Dec 23, 2022
Non-Imaging Transient Reconstruction And TEmporal Search (NITRATES)

Non-Imaging Transient Reconstruction And TEmporal Search (NITRATES) This repo contains the full NITRATES pipeline for maximum likelihood-driven discov

13 Nov 08, 2022
Multimodal Temporal Context Network (MTCN)

Multimodal Temporal Context Network (MTCN) This repository implements the model proposed in the paper: Evangelos Kazakos, Jaesung Huh, Arsha Nagrani,

Evangelos Kazakos 13 Nov 24, 2022