Integrating Distributed Component-Based Systems Through Deep Reinforcement Learning

Itay Cohen, Doron Peled

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Modern system design and development often consists of combining different components developed by separate vendors under some known constraints that allow them to operate together. Such a system may further benefit from further refinement when the components are integrated together. We suggest a learning-open architecture that employs deep reinforcement learning performed under weak assumptions. The components are “black boxes”, where their internal structure is not known, and the learning is performed in a distributed way, where each process is aware only on its local execution information and the global utility value of the system, calculated after complete executions. We employ the proximal policy optimization (PPO) as our learning architecture adapted to our case of training control for black box components. We start by applying the PPO architecture to a simplified case, where we need to train a single component that is connected to a black box environment; we show a stark improvement when compared to a previous attempt. Then we move to study the case of multiple components.

Original languageEnglish
Title of host publicationBridging the Gap Between AI and Reality - 1st International Conference, AISoLA 2023, Proceedings
EditorsBernhard Steffen
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages23
ISBN (Print)9783031460012
StatePublished - 2024
Event1st International Conference on Bridging the Gap between AI and Reality, AISoLA 2023 - Crete, Greece
Duration: 23 Oct 202328 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14380 LNCS


Conference1st International Conference on Bridging the Gap between AI and Reality, AISoLA 2023

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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