Neuroscience-Inspired Reinforcement Learning Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

RL was born from the dialogue with neuroscience. These works trace dopamine, predictive coding, and hippocampal replay into algorithms.

Neuroscience-Inspired Reinforcement Learning: 10 key papers

1. Neuroscience-Inspired Artificial Intelligence
   Hassabis et al. Neuron 2017.
2. Prefrontal cortex as a meta-reinforcement learning system
   Wang et al. Nature Neuroscience 2018.
3. A distributional code for value in dopamine-based reinforcement learning
   Dabney et al. Nature 2020.
4. Vector-based navigation using grid-like representations in artificial agents
   Banino et al. Nature 2018.
5. The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation
   Whittington et al. Cell 2020.
6. Deep Reinforcement Learning and its Neuroscientific Implications
   Botvinick et al. arXiv 2020.
7. Building Machines That Learn and Think Like People
   Lake et al. arXiv 2016.
8. What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated
   Kumaran et al. Trends in Cognitive Sciences 2016.
9. Towards an integration of deep learning and neuroscience
   Marblestone et al. arXiv 2016.
10. Meta-learning in natural and artificial intelligence
    Wang. arXiv 2020.

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• Code Generation & Program Synthesis with RL