Novelty producing synaptic plasticity

Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George Fletcher, Mykola Pechenizkiy

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Abstract

A learning process with the plasticity property often requires reinforcement signals to guide the process. However, in some tasks (e.g. maze-navigation), it is very difficult to measure the performance of an agent to provide reinforcements, since the position of the goal is not known. This requires finding the correct behavior among a vast number of possible behaviors without having any feedback. In these cases, an exhaustive search may be needed. However, this might not be feasible especially when optimizing artificial neural networks in continuous domains. In this work, we introduce novelty producing synaptic plasticity (NPSP), where we evolve synaptic plasticity rules to produce as many novel behaviors as possible to find the behavior that can solve the problem. We evaluate the NPSP on deceptive maze environments that require the achievement of subgoals. Our results show that the proposed NPSP produces more novel behaviors compared to Random Search and Random Walk.

Original languageEnglish
Title of host publicationGECCO '20
Subtitle of host publicationProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
EditorsCarlos Artemio Coello Coello
PublisherAssociation for Computing Machinery (ACM)
Pages93-94
Number of pages2
ISBN (Electronic)978-1-4503-7127-8
ISBN (Print)9781450371278
DOIs
Publication statusPublished - 8 Jul 2020
Externally publishedYes
EventGenetic and Evolutionary Computation Conference, GECCO 2020 - Online Event, Cancun, Mexico
Duration: 8 Jul 202012 Jul 2020

Conference

ConferenceGenetic and Evolutionary Computation Conference, GECCO 2020
Abbreviated titleGECCO 2020
CountryMexico
CityCancun
Period8/07/2012/07/20

Keywords

  • Neuro-evolution
  • Novelty
  • Synaptic plasticity
  • Unsupervised learning

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