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Robust optimization through neuroevolution.

Paolo Pagliuca1, Stefano Nolfi1

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Summary
This summary is machine-generated.

This study introduces a novel method for evolving robust neural network controllers that perform well in new environments without adaptation. The approach is effective and computationally tractable, outperforming human-designed controllers in complex tasks.

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Evolutionary Computation

Background:

  • Neural network controllers often struggle with environmental variations, requiring adaptation.
  • Developing controllers robust to environmental changes is crucial for real-world applications.

Purpose of the Study:

  • To propose a method for evolving neural network controllers that are robust to environmental variations.
  • To demonstrate the effectiveness and computational tractability of the proposed method.

Main Methods:

  • Specifying fitness evaluation for candidate solutions.
  • Defining environmental variation strategies during evolution.
  • Utilizing evolutionary algorithms, particularly CMA-ES and xNES, for parameter distribution optimization.
  • Establishing criteria for identifying the best solution.

Main Results:

  • The method significantly improves performance on the double-pole balancing problem.
  • Controllers evolved using this method outperform human-designed controllers in a car racing problem.
  • Effective solutions were generated for a swarm robotic problem.
  • CMA-ES and xNES were identified as optimal algorithms for evolving robust neural network controllers.

Conclusions:

  • The proposed method is effective and computationally tractable for evolving robust neural network controllers.
  • This approach enables controllers to operate effectively in new conditions immediately.
  • Evolutionary strategies optimizing parameter distributions are superior for this task.