Deep reinforcement learning to study combinatorial expansion of a behavior repertoire.

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Abstract

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References

  1. Kohler, W. The Mentality of Apes (trans Winter, E.) (Liveright, 1976). This book presents the intelligent behavior repertoires of chimpanzees.
  2. Epstein, R., Kirshnit, C. E., Lanza, R. P. & Rubin, L. C. ‘Insight’ in the pigeon: antecedents and determinants of an intelligent performance. Nature 308, 61–62 (1984). This paper reports the sudden acquisition of a problem solution in pigeons by combining pre-learned motor skills. [DOI: 10.1038/308061a0]
  3. Arthur, W. B. The Nature of Technology: What It is and How It Evolves (Free Press, 2009). This book presents the concept of combinatorial evolution of technology.
  4. Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J. & Kurth-Nelson, Z. Deep reinforcement learning and its neuroscientific implications. Neuron 107, 603–616 (2020). This paper emphasizes the benefits of collaborative efforts between deep reinforcement learning and neuroscience. [DOI: 10.1016/j.neuron.2020.06.014]
  5. Haarnoja, T. et al. Composable deep reinforcement learning for robotic manipulation. 2018 IEEE International Conf. on Robotics and Automation (ICRA) 6244–6251 (2018). This paper reports a deep reinforcement learning-based theoretical framework for combinatorial policy composition in machines.

MeSH Term

Neural Networks, Computer
Reinforcement, Psychology
Neurosciences

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