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Automated curriculum learning for embodied agents a neuroevolutionary approach.

Nicola Milano1, Stefano Nolfi2

  • 1Institute of Cognitive Science and Technologies, National Research Council, Rome, Italy. nicola.milano@istc.cnr.it.

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Summary
This summary is machine-generated.

This study introduces curriculum learning for evolutionary training of embodied agents, automatically adjusting environmental difficulty to improve agent abilities and robustness. The novel method enhances agent performance without needing domain expertise or extra hyperparameters.

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Area of Science:

  • Artificial Intelligence
  • Evolutionary Computation
  • Machine Learning

Background:

  • Embodied agents require robust training methods to adapt to diverse environmental conditions.
  • Current evolutionary training often lacks adaptive difficulty adjustment, hindering optimal performance.

Purpose of the Study:

  • To develop an automated curriculum learning algorithm for evolutionary training of embodied agents.
  • To enhance agent adaptability and robustness by dynamically adjusting environmental difficulty.

Main Methods:

  • Implemented a curriculum learning algorithm that automatically selects environmental conditions for agent evaluation.
  • The algorithm adjusts difficulty based on the current ability and weaknesses of evolving agents.
  • No domain knowledge or additional hyperparameters were required.

Main Results:

  • The proposed method significantly outperforms conventional learning approaches on two benchmark problems.
  • Generated agents demonstrated superior robustness to environmental variations.
  • Solutions effectively coped with diverse and challenging environmental conditions.

Conclusions:

  • Automated curriculum learning is an effective extension for evolutionary training of embodied agents.
  • The method promotes the development of highly adaptable and robust artificial agents.
  • This approach offers a generalizable solution for training agents in complex, variable environments.