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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.

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

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

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 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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