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Download PDF by : Reinforcement Learning and Approximate Dynamic Programming

ISBN-10: 111845393X

ISBN-13: 9781118453933

Reinforcement studying (RL) and adaptive dynamic programming (ADP) has been probably the most serious study fields in technological know-how and engineering for contemporary complicated structures.

This publication describes the most recent RL and ADP options for selection and keep an eye on in human engineered structures, overlaying either unmarried participant choice and regulate and multi-player video games.

Edited by way of the pioneers of RL and ADP learn, the booklet brings jointly rules and strategies from many fields and offers an immense and well timed tips on controlling a large choice of structures, reminiscent of robots, business procedures, and financial decision-making.

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Read or Download Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (IEEE Press Series on Computational Intelligence, Volume 17) PDF

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Additional resources for Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (IEEE Press Series on Computational Intelligence, Volume 17)

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14) i=1 This approximation is still governed, in principle, by Barron’s results on linear basis function approximators, but if the users supply basis functions suited to his particular problem, it might allow better performance in practice. ” It is also analogous to the use of user-defined “features,” like HOG or SIFT features, in traditional image processing. It also opens the door to many special cases of general-purpose ADP methods, and new specialpurpose methods for the linear case, such as the use of linear programming to estimate the weights W [64].

Besides approximating J ∗ (X) or λ(X), it is also possible to approximate: J (X(t), u(t)) = Q(X(t), u(t)) = U(X(t), u(t)) + Max J ∗ (X(t + 1))/(1 + r) . 12) Note that J and Q are the same thing. D. ” In the same year [34], independently, I proposed the use of universal approximators to approximate J , in action-dependent HDP. Actiondependent HDP was the method used by White and Sofge [4] in their breakthrough control for the continuous production of thermoplastic carbon–carbon parts, a technology which is now of enormous importance to the aircraft industry, as in the recent breakthrough commercial airplane, the Boeing 787.

8) 10 REINFORCEMENT LEARNING AND APPROXIMATE DYNAMIC PROGRAMMING This leads directly to the key equation derived by Richard Bellman (in updated notation): J ∗ (X(t)) = max U(X(t), u(t)) + J ∗ (X(t + 1))/(1 + r) . 5), and the set of allowed values which u(t) may be taken from. ) With that information, it is possible to solve for the function J ∗ which satisfies this equation. The original theorems of DP tell us that J ∗ exists, and that maximizing U + J ∗ /(1 + r) as shown in the Bellman equation gives us an optimal policy.

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Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (IEEE Press Series on Computational Intelligence, Volume 17)

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