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RADDICS SIGNED

Reliable Data-Driven Decision Making in Cyber-Physical Systems

Total Cost €

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EC-Contrib. €

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Partnership

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 RADDICS project word cloud

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

abstraction    rl    proposition    opt    fundamental    imitation    efficiency    deep    safe    electron    optimal    bridging    tuning    closed    simulations    extensively    robotic    optimizing    tackle    seek    nonparametric    employing    bootstrapping    motivated    breakthrough    dimensional    specified    near    strive    unsafe    breakthroughs    active    performance    cyber    rarely    explored    pursuing    dramatic    dynamics    environments    erc    successes    safely    dangerous    pushes    laser    simulated    photovoltaic    reasoning    estimation    robustness    reliability    visited    world    energy    probability    pursue    guaranteeing    powerplants    perspective    regularity    initial    bayesian    time    first    exploration    physical    decision    models    unknown    priori    fidelity    episodic    explicitly    ideas    cps    games    powerful    extremely    gaussian    rethink    learning    overcome    optimization    interdisciplinary    experiments    power    platforms    limitations    free    data    assumptions    accurate    computing    boundary    provably    policies    theory   

Project "RADDICS" data sheet

The following table provides information about the project.

Coordinator
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH 

Organization address
address: Raemistrasse 101
city: ZUERICH
postcode: 8092
website: https://www.ethz.ch/de.html

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 Switzerland [CH]
 Total cost 1˙996˙500 €
 EC max contribution 1˙996˙500 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-COG
 Funding Scheme ERC-COG
 Starting year 2019
 Duration (year-month-day) from 2019-01-01   to  2023-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH CH (ZUERICH) coordinator 1˙996˙500.00

Map

 Project objective

This ERC project pushes the boundary of reliable data-driven decision making in cyber-physical systems (CPS), by bridging reinforcement learning (RL), nonparametric estimation and robust optimization. RL is a powerful abstraction of decision making under uncertainty and has witnessed dramatic recent breakthroughs. Most of these successes have been in games such as Go - well specified, closed environments that - given enough computing power - can be extensively simulated and explored. In real-world CPS, however, accurate simulations are rarely available, and exploration in these applications is a highly dangerous proposition.

We strive to rethink Reinforcement Learning from the perspective of reliability and robustness required by real-world applications. We build on our recent breakthrough result on safe Bayesian optimization (SAFE-OPT): The approach allows - for the first time - to identify provably near-optimal policies in episodic RL tasks, while guaranteeing under some regularity assumptions that with high probability no unsafe states are visited - even if the set of safe parameter values is a priori unknown.

While extremely promising, this result has several fundamental limitations, which we seek to overcome in this ERC project. To this end we will (1) go beyond low-dimensional Gaussian process models and towards much richer deep Bayesian models; (2) go beyond episodic tasks, by explicitly reasoning about the dynamics and employing ideas from robust control theory and (3) tackle bootstrapping of safe initial policies by bridging simulations and real-world experiments via multi-fidelity Bayesian optimization, and by pursuing safe active imitation learning.

Our research is motivated by three real-world CPS applications, which we pursue in interdisciplinary collaboration: Safe exploration of and with robotic platforms; tuning the energy efficiency of photovoltaic powerplants and safely optimizing the performance of a Free Electron Laser.

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

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