Learning Algorithms for Networks with Internal and External Feedback

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On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “M has m+n+o input units to receive all(t) at time t < tdeath, and m + n output units to produce a prediction pred(t + 1) ∈ R of sense(t + 1) [223, 226, 222, 227].”
One Big Net For Everything (2018, methodology): “For this purpose, ONE has m + n special output units to produce for t t T a prediction pred ( t ) ∈ R m + n of sense ( t + 1) [55, 56, 59, 54, 60] from ONE’s previous observations and actions, which are in principle accessible to ONE through (recurrent) connections.”
Learning to Generate Artificial Fovea Trajectories for Target Detection (1991, background): “In di erent contexts and with di erent degrees of generality the basic principle for credit assignment by system realization and `gradient descent through a frozen model network' has been previously described by Werbos [19], Jordan [2], Munro [5], Robinson & Fallside [8], Nguyen & Widrow [6], and Sc”
Making the World Di erentiable: On Using Self-Supervised Fully Recurrent Neural Networks for Dynamic Reinforcement Learning (1990, unknown): “The Algorithm The reinforcement learning algorithm described in this section attempts to be a very general one [28].”