Accelerated neural evolution through cooperatively coevolved synapses

Faustino Gomez, Jürgen Schmidhuber, Risto Miikkulainen

Research output: Contribution to journalArticlepeer-review

241 Scopus citations


Many complex control problems require sophisticated solutions that are not amenable to traditional controller design. Not only is it difficult to model real world systems, but often it is unclear what kind of behavior is required to solve the task. Reinforcement learning (RL) approaches have made progress by using direct interaction with the task environment, but have so far not scaled well to large state spaces and environments that are not fully observable. In recent years, neuroevolution, the artificial evolution of neural networks, has had remarkable success in tasks that exhibit these two properties. In this paper, we compare a neuroevolution method called Cooperative Synapse Neuroevolution (CoSyNE), that uses cooperative coevolution at the level of individual synaptic weights, to a broad range of reinforcement learning algorithms on very difficult versions of the pole balancing problem that involve large (continuous) state spaces and hidden state. CoSyNE is shown to be significantly more efficient and powerful than the other methods on these tasks.
Original languageEnglish (US)
Pages (from-to)937-965
Number of pages29
JournalJournal of Machine Learning Research
StatePublished - May 1 2008
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Statistics and Probability
  • Control and Systems Engineering


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