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Showing 1–4 of 4 results
Advanced filters: Author: Jeff Clune Clear advanced filters
  • A reinforcement learning algorithm that explicitly remembers promising states and returns to them as a basis for further exploration solves all as-yet-unsolved Atari games and out-performs previous algorithms on Montezuma’s Revenge and Pitfall.

    • Adrien Ecoffet
    • Joost Huizinga
    • Jeff Clune
    Research
    Nature
    Volume: 590, P: 580-586
  • An intelligent trial-and-error learning algorithm is presented that allows robots to adapt in minutes to compensate for a wide variety of types of damage.

    • Antoine Cully
    • Jeff Clune
    • Jean-Baptiste Mouret
    Research
    Nature
    Volume: 521, P: 503-507
  • Deep neural networks have become very successful at certain machine learning tasks partly due to the widely adopted method of training called backpropagation. An alternative way to optimize neural networks is by using evolutionary algorithms, which, fuelled by the increase in computing power, offers a new range of capabilities and modes of learning.

    • Kenneth O. Stanley
    • Jeff Clune
    • Risto Miikkulainen
    Reviews
    Nature Machine Intelligence
    Volume: 1, P: 24-35
  • It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.

    • Dhireesha Kudithipudi
    • Mario Aguilar-Simon
    • Hava Siegelmann
    Reviews
    Nature Machine Intelligence
    Volume: 4, P: 196-210