myGym

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00356339" target="_blank" >RIV/68407700:21730/21:00356339 - isvavai.cz</a>

Výsledek na webu

<a href="https://github.com/incognite-lab/myGym" target="_blank" >https://github.com/incognite-lab/myGym</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

myGym

Popis výsledku v původním jazyce

Toolkit suitable for fast prototyping of neural networks in the area of robotic manipulation and navigation. Our toolbox is fully modular, so that you can train your network with different robots, in several environments and on various tasks. You can also create a curriculum of tasks with increasing complexity and test your network on them. We also included an automatic evaluation and benchmark tool for your developed model. We have pretained the Yolact network for visual recognition of all objects in the simulator, so that you can reward your networks based on visual sensors only. We keep training the current state-of-the-art algorithms to provide baselines for the tasks in the toolbox. There is also a leaderboard showing algorithms with the best generalization capability, tested on the tasks in our basic curriculum.

Název v anglickém jazyce

myGym

Popis výsledku anglicky

Toolkit suitable for fast prototyping of neural networks in the area of robotic manipulation and navigation. Our toolbox is fully modular, so that you can train your network with different robots, in several environments and on various tasks. You can also create a curriculum of tasks with increasing complexity and test your network on them. We also included an automatic evaluation and benchmark tool for your developed model. We have pretained the Yolact network for visual recognition of all objects in the simulator, so that you can reward your networks based on visual sensors only. We keep training the current state-of-the-art algorithms to provide baselines for the tasks in the toolbox. There is also a leaderboard showing algorithms with the best generalization capability, tested on the tasks in our basic curriculum.

Druh

R - Software

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000766" target="_blank" >EF16_019/0000766: Inženýrské aplikace fyziky mikrosvěta</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Interní identifikační kód produktu

mygym 2.0

Technické parametry

System requirements Ubuntu 18.04, 20.04 Python 3 GPU acceleration strongly recommended

Ekonomické parametry

Vytvořeno v rámci projektu INAFYM

IČO vlastníka výsledku

68407700

Název vlastníka

České vysoké učení technické v Praze / Český institut informatiky, robotiky a kybernetiky