Efficient Parallelized Simulation of Cyber-Physical Systems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00376366" target="_blank" >RIV/68407700:21730/24:00376366 - isvavai.cz</a>

Result on the web

<a href="https://openreview.net/pdf?id=VzKXbCzNoU" target="_blank" >https://openreview.net/pdf?id=VzKXbCzNoU</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Efficient Parallelized Simulation of Cyber-Physical Systems

Original language description

Advancements in accelerated physics simulations have greatly reduced training times for reinforcement learning policies, yet the conventional step-by-step agent-simulator interac tion undermines simulation accuracy. In the real world, interactions are asynchronous, with sensing, acting and processing happening simultaneously. Failing to capture this widens the sim2real gap and results in suboptimal real-world performance. In this paper, we address the challenges of simulating realistic asynchronicity and delays within parallelized simula tions, crucial to bridging the sim2real gap in complex cyber-physical systems. Our approach efficiently parallelizes cyber-physical system simulations on accelerator hardware, including physics, sensors, actuators, processing components and their asynchronous interactions. We extend existing accelerated physics simulations with latency simulation capabilities by con structing a `supergraph’ that encodes all data dependencies across parallelized simulation steps, ensuring accurate simulation. By finding the smallest supergraph, we minimize re dundant computation. We validate our approach on two real-world systems and perform an extensive ablation, demonstrating superior performance compared to baseline methods.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Transactions on Machine Learning Research

ISSN

2835-8856

e-ISSN

2835-8856

Volume of the periodical

2024

Issue of the periodical within the volume

May

Country of publishing house

DE - GERMANY

Number of pages

18

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85219527413