Stochastic Systems: Estimation, Identification, and Adaptive Control

By P. R. Kumar, Pravin Varaiya. 1986

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Citations in the corpus

Reinforcement Learning with Replacing Eligibility Traces (2005, neutral): “For this reason, this estimate is sometimes also referred to as the certainty equivalent estimate(e.g., Kumar and Varaiya, 1986).5.”
A Kernel Loss for Solving the Bellman Equation (2019, methodology): “In this case, the TD solution coincides with that of a model-based approach (Parr et al., 2008) known as certainty equivalence (Kumar & Varaiya, 1986).”
Feedback Systems: An Introduction for Scientists and Engineers (2008, unknown): “This section assumes basic knowledge of random variables and stochastic processes; see Kumar and Varaiya [131] or Åström [15] for the required material.”
Cyber–Physical Systems: A Perspective at the Centennial (2012, background): “[29], linear systems [30], nonlinear control [31], stochastic systems [32], adaptive control [33], robust control [34], infinite dimensional systems [35], decentralized control of complex systems [36], discrete event systems [37], and even attempts at integrating automata theory and control [38].”
Reinforcement Learning: An Introduction (1998, background): “MDPs are also studied under the heading of stochastic optimal control, where adaptive optimal control methods are most closely related to reinforcement learning (e.g., Kumar, 1985; Kumar and Varaiya, 1986).”
Rationality and Intelligence: A Brief Update (2013, background): “domains, stochastic optimal control theory (Kumar and Varaiya, 1986) has solved some restricted classes of problems, while many others remain open.”
REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs (2009, background): “The problem of simultaneous estimation and control of MDPs has been studied in the control theory [Kumar and Varaiya, 1986], operations research [Burnetas and Katehakis, 1997] and machine learning communities.”
Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks (2007, methodology): “, in [5]) that the optimal control law minimizing (3) is given by”
Incremental self-improvement for life-time multi-agent reinforcement learning (1996, background): “Some reinforcement learning algorithms are guaranteed to converge in certain limited, nonchanging environments, e.g., (Kumar and Varaiya, 1986; Barto, 1989; Watkins and Dayan, 1992; Williams, 1992; Jaakkola et al., 1995; Kaelbling et al., 1995; Ring, 1994).”
Algorithms for Sequential Decision Making (1996, background): “In the certainty-equivalence approach [86], an optimal policy for the estimated model is found at each step.”