Titlebook: Handbook of Reinforcement Learning and Control; Kyriakos G. Vamvoudakis,Yan Wan,Derya Cansever Book 2021 Springer Nature Switzerland AG 20

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Computational Intelligence in Uncertainty Quantification for Learning Control and Differential Gamesring the significant computation load needed to evaluate them in real-time decision processes. This chapter describes the use of computationally effective uncertainty evaluation methods for adaptive optimal control, including learning control and differential games. Two uncertainty evaluation method

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Modeling and Mitigating Link-Flooding Distributed Denial-of-Service Attacks via Learning in Stackelbess the challenge that the adversary can observe the routing strategy before assigning attack traffic, we model the conflict between routing and attack as a Stackelberg game. For a general class of adversaries, we establish a characterization of an optimal attack strategy that reduces the search spa

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Adithya M. Devraj,Ana Bušić,Sean Meyn of evening out perceived injustices. The ‘Pillars of Society’ and the ‘Balance Artists’ believe in the welfare state, and each provide their version of a fair share. It is a perception game in terms of paying/avoiding/evading taxation that is addressed as contributive and distributive balancing acts.

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Max L. Greene,Patryk Deptula,Rushikesh Kamalapurkar,Warren E. Dixonvision was made for expectant and nursing mothers, and for children; in addition, fair shares for all were ensured through rationing. The benefits of this courageous experiment are still evident today. Indeed, now it is obesity rather than undernourishment that has become a problem among children.

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Reinforcement Learning-Based Model Reduction for Partial Differential Equations: Application to the (FEM), one may end up with a very large discretization space, which incurs large computation times. Because of this complexity, it is often hard to use PDEs to analyze, predict, or control these systems in real time.

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Model-Free Linear Quadratic Regulatorof convexity, it converges to the globally optimal LQR solution at a linear rate. These results demonstrate that for a model-free method that utilizes two-point gradient estimates, the simulation time and the total number of function evaluations required for achieving .-accuracy are both ..