Backpropagation and neurocontrol: a review and prospectus

By Paul J. Werbos. 1989

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Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “a separate FNN called M can learn to become C’s world model through system identiﬁcation , predicting C’s inputs from previous actions and inputs (e.g., Werbos, 1981, 1987; Munro, 1987; Jordan, 1988; Werbos, 1989b,a; Robinson and Fallside, 1989; Jordan and Rumelhart, 1990; Schmidhuber, 1990d; Narend”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “The first combination of an RL RNN C and an UL RNN M was ours and dates back to 1990 [223, 222, 226, 227], generalizing earlier similar controller/model systems (CM systems) based on FNNs [298, 179]; compare related work [177, 119, 301, 300, 209, 120, 178, 302, 73, 45, 144, 166, 153, 196, 60][245, S”
Reinforcement Learning Upside Down: Don't Predict Rewards - Just Map Them to Actions (2019, background): “Using SL for certain aspects of RL dates back to the 1980s and 90s [70, 31, 22, 73, 72, 39, 32].”
Making the World Di erentiable: On Using Self-Supervised Fully Recurrent Neural Networks for Dynamic Reinforcement Learning (1990, background): “An overview of adaptive system identi cation and Werbos' `Heuristic Dynamic Programming' (related to Barto and Sutton's `adaptive critic') as well as references to Werbos' earlier relevant work on reinforcement learning and control are given in [43](see also [12]).”
A possibility for implementing curiosity and boredom in model-building neural controllers (1991, background): “[12] P. J. Werbos.”
Reinforcement Learning in Continuous State and Action Spaces (2012, methodology): “Both approaches have often been used in reinforcement learning with considerable success (Sutton, 1988; Werbos, 1989b,a, 1990; Whitley et al, 1993; Tesauro, 1994, 1995; Moriarty andMiikkulainen, 1996; Moriarty et al, 1999; Whiteson and Stone, 2006; Wierstra et al, 2008; Rückstieß et al, 2010).”
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”