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Embodied intelligence via learning and evolution

Author

Listed:
  • Agrim Gupta

    (Stanford University)

  • Silvio Savarese

    (Stanford University)

  • Surya Ganguli

    (Stanford University
    Stanford University
    Stanford University)

  • Li Fei-Fei

    (Stanford University
    Stanford University)

Abstract

The intertwined processes of learning and evolution in complex environmental niches have resulted in a remarkable diversity of morphological forms. Moreover, many aspects of animal intelligence are deeply embodied in these evolved morphologies. However, the principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control, remain elusive, because performing large-scale in silico experiments on evolution and learning is challenging. Here, we introduce Deep Evolutionary Reinforcement Learning (DERL): a computational framework which can evolve diverse agent morphologies to learn challenging locomotion and manipulation tasks in complex environments. Leveraging DERL we demonstrate several relations between environmental complexity, morphological intelligence and the learnability of control. First, environmental complexity fosters the evolution of morphological intelligence as quantified by the ability of a morphology to facilitate the learning of novel tasks. Second, we demonstrate a morphological Baldwin effect i.e., in our simulations evolution rapidly selects morphologies that learn faster, thereby enabling behaviors learned late in the lifetime of early ancestors to be expressed early in the descendants lifetime. Third, we suggest a mechanistic basis for the above relationships through the evolution of morphologies that are more physically stable and energy efficient, and can therefore facilitate learning and control.

Suggested Citation

  • Agrim Gupta & Silvio Savarese & Surya Ganguli & Li Fei-Fei, 2021. "Embodied intelligence via learning and evolution," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25874-z
    DOI: 10.1038/s41467-021-25874-z
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    References listed on IDEAS

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    1. Anthony Zador & Sean Escola & Blake Richards & Bence Ölveczky & Yoshua Bengio & Kwabena Boahen & Matthew Botvinick & Dmitri Chklovskii & Anne Churchland & Claudia Clopath & James DiCarlo & Surya Gangu, 2023. "Catalyzing next-generation Artificial Intelligence through NeuroAI," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    2. Xiao-Yang Liu & Ziyi Xia & Jingyang Rui & Jiechao Gao & Hongyang Yang & Ming Zhu & Christina Dan Wang & Zhaoran Wang & Jian Guo, 2022. "FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning," Papers 2211.03107, arXiv.org.
    3. Xiao-Yang Liu & Jingyang Rui & Jiechao Gao & Liuqing Yang & Hongyang Yang & Zhaoran Wang & Christina Dan Wang & Jian Guo, 2021. "FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance," Papers 2112.06753, arXiv.org, revised Mar 2022.

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