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Reliable Data-Driven Decision Making in Cyber-Physical Systems

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.

Host institution

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

Address

Raemistrasse 101
8092 Zuerich

Switzerland

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 1 996 500

Beneficiaries (1)

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EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

Switzerland

EU Contribution

€ 1 996 500

Project information

Grant agreement ID: 815943

Status

Ongoing project

  • Start date

    1 January 2019

  • End date

    31 December 2023

Funded under:

H2020-EU.1.1.

  • Overall budget:

    € 1 996 500

  • EU contribution

    € 1 996 500

Hosted by:

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

Switzerland