Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters.

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Learning to reinforcement learn (2016, background): “…in neuroscience but only in a narrow sense, according to which meta-learning adjusts scalar hyperparameters such as the learning rate or softmax inverse temperature (Khamassi et al., 2011; 2013; Kobayashi et al., 2009; Lee and Wang, 2009; Schweighofer and Doya, 2003; Soltani et al., 2006).”
Reinforcement Learning, Fast and Slow. (2019, unknown): “In this model, slow, incremental learning processes shape recurrent brain circuits into algorithms that exploit consistent structure in the environment to learn efficiently (for related work, see [86,103,104]).”
Hippocampal replays under the scrutiny of reinforcement learning models. (2018, methodology): “science, the inference phase of MF-RL simply consists in reading from a table the Q values of a small finite set of actions, so as to compare them and make a decision (O’Doherty et al., 2004; Pessiglione et al., 2006; Palminteri et al., 2015; Bavard et al., 2018).”