Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches

By David A. White, D. Sofge. 1992

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “…& Kawato, 1993; Jordan, 1988; Jordan & Rumelhart, 1990; Levin & Narendra, 1995; Ljung, 1998; Miller, Werbos, & Sutton, 1995; Munro, 1987; Narendra & Parthasarathy, 1990; Prokhorov, Puskorius, & Feldkamp, 2001; Robinson & Fallside, 1989; Schmidhuber, 1990d; Werbos, 1981, 1987, 1989a, 1989b, 1992).”
A Survey of Reinforcement Learning Informed by Natural Language (2019, methodology): “Since their inception, RL algorithms have been successful in applications such as continuous control [White and Sofge, 1992], dialogue systems [Singh et al. , 2002], and board games [ Tesauro, 1995 ] .”
Multiple Forms of Value Learning and the Function of Dopamine (2009, background): “…3.2, these action-values can be learned by model-free reinforcement learning algorithms such as the actor-critic Balleine, Daw & O'Doherty – 23 (Barto, 1992), in which prediction error signals generated by a critic that evaluates the expected reward available in each state of the world are…”
A Bayesian Sampling Approach to Exploration in Reinforcement Learning (2009, background): “Undirected (Thrun, 1992) approaches take exploratory actions, but without regard to what parts of their environment models remain uncertain.”
Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms (2000, background): “All such algorithms face a tradeoff between exploitation and exploration (Thrun, 1992; Kumar & Varaiya, 1986; Dayan & Sejnowski, 1996), i.e., between choosing actions that are best according to the current state of knowledge, and actions that are not the current best but improve the state of…”
An empirical evaluation of interval estimation for Markov decision processes (2004, background): “Many approaches to exploration in MDPs have been proposed (Thrun, 1992; Wiering & Schmidhuber, 1998; Dearden et al., 1999; Meuleau & Bourgine, 1999; Wyatt, 2001).”
A unified framework for stochastic optimization (2019, background): “Important references are the edited olumes ( White & Sofge, 1992 ) and ( Si, Barto, Powell, & Wunsch, 004 ) which highlighted what had already become a popular aproach using neural networks to approximate both policies (“actor ets”) and value functions (“critic nets”).”