Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Language: English

Pages: 648

ISBN: 111810420X

Format: PDF / Kindle (mobi) / ePub


Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.

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18.3 identifies the major classes of policies, which we then use to identify opportunities for optimal learning. 18.2 Modeling There are five core components of any stochastic, dynamic system. These include The State Variable St: The state variable captures the information available at time t. Decisions, Actions, or Controls: We use the notation at to represent discrete action spaces, although many problems exhibit vector-valued decisions or controls. Exogenous Information: We let Wt represent

variables within systems often studied by social scientists, are also treated in this general timescales case. Key to the development of the backpropagation algorithm is the notion of a chain rule for ordered derivatives. This chain rule in the timescales calculus is proven and what an ordered derivative means within this new mathematics is defined. 21.3.1 Ordered Derivatives In his PhD dissertation [11, 12], Paul Werbos discussed the idea of an ordered derivative distinct from the notion of a

of certain parameter(s). The functional form of the approximator is also called an architecture. Architectures based on linear function approximators have been well studied in the literature because algorithms such as temporal difference (TD) learning [7] have been shown convergent when linear architectures are used [8, 9]. Here, the value of a given state is approximated using the scalar product of the parameter vector with the feature associated with the state. The feature usually quantifies

smallest component of the weight vector is replaced by independent and uniformly generated random numbers between 0 and 1. Thus, while the worst performer (among the columns of the feature matrix) is replaced by the normalized value function estimate, the second-worst performer is replaced with a random search direction to aid in exploration of better features and thereby a better subspace than the previous. The remaining columns of the feature matrix are left unchanged. The TD algorithm is then

Management Science and Engineering, Stanford University, Stanford, CA, USA Abstract In this chapter, a new ADP algorithm integrating (1) systematic basis function construction, (2) a linear programming (LP) approach in dynamic programming (DP), (3) adaptive basis function selection, and (4) bootstrapping is developed and applied to oil production problems. The procedure requires the solution of a large-scale dynamic system, which is accomplished using a subsurface flow simulator, for function

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