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Data-Efficient Scalable Reinforcement Learning for Practical Robotic Environments

Total Cost €


EC-Contrib. €






 DESlRE project word cloud

Explore the words cloud of the DESlRE project. It provides you a very rough idea of what is the project "DESlRE" about.

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Project "DESlRE" data sheet

The following table provides information about the project.


Organization address
city: Munich
postcode: 80539

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Total cost 159˙460 €
 EC max contribution 159˙460 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2017
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2018
 Duration (year-month-day) from 2018-04-01   to  2020-03-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

The robotics industry is in the process of greater adoption of machine learning. Recent reinforcement learning (RL) and AI breakthroughs, such as AlphaGo, rely on collecting large amounts of data. Such methods are unsuitable for real robots which often can only afford a few trials. Moreover, some states are unsafe to explore, e.g. running over a cliff. Conversely, works such as PILCO combine Bayesian models with model-based RL to improve data efficiency. Those frameworks typically thrive in small data regimes. The goal of this project is to develop RL algorithms that scale to high dimensions while learning with less data. The main pillars of our methodology are RL, recurrent networks, Bayesian methods, embodied exploration, and optimization. To tackle the data efficiency, we adopt model-based RL approaches. We plan to combine representation learning and dynamics in a single model, leading to high predictive power and low-dimensional internal state spaces. Notably, we use methods that can learn disentangled representations, e.g. infoGAN. In practical robots, effective exploration is a real problem in current approaches. We want to leverage recent works in embodied exploration by the host group which allows various real-world robots to explore their capabilities in minutes of interaction. I received my Ph.D. for work in optimization with Dr. William Hager. I also conducted postdoctoral research in machine learning. The Autonomous Learning group is led by Dr. Georg Martius, who has previously studied artificial intrinsic motivation, the self-organized exploration of sensorimotor coordination via information theory, and internal model learning. He also developed the robotics environment LPZRobots. I will gain extensive experience in practical robotics, embodied exploration, and information theory through the collaboration and mature as an advanced AI researcher. Both Dr. Martius and I have a track record of publishing code online. We will continue this effort.

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The information about "DESLRE" are provided by the European Opendata Portal: CORDIS opendata.

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