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Deep reinforcement learning to study combinatorial expansion of a behavior repertoire


Despite rich behavioral evidence, it is unclear how the brain expands its behavior repertoire. By building theoretical models with a deep reinforcement learning algorithm, I show that the brain composes a behavior to solve a novel task by combining previously acquired skills and augmenting their variability.

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Fig. 1: Behavior composition via transfer of combinatorial neural representations of action values.


  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

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This is a summary of: Makino, H. Arithmetic value representation for hierarchical behavior composition. Nat. Neurosci. (2022).

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Deep reinforcement learning to study combinatorial expansion of a behavior repertoire. Nat Neurosci 26, 2–3 (2023).

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