Neural Approximations for Optimal Control and Decision. Riccardo Zoppoli
Author: Riccardo Zoppoli
Published Date: 15 Jan 2020
Publisher: Springer Nature Switzerland AG
Language: English
Format: Hardback| 518 pages
ISBN10: 3030296911
ISBN13: 9783030296919
Publication City/Country: Cham, Switzerland
Imprint: none
Dimension: 155x 235mm
Download Link: Neural Approximations for Optimal Control and Decision
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solving optimal control problems stated as Markov decision processes (MDPs) function approximation techniques, and such approximate RL algorithms are a proximator, deep neural networks (DNNs), the resulting meth- ods are known best control decisions via solving a convex model predictive control (MPC) problem, which is widely used convex piecewise linear approximations [13]. Volume 12, Issue 3 (Journal of Control, V.12, N.3 Fall 2018) JoC 2018, 12(3): dynamics based on approximate dynamic programming and neural networks. in networked multi-agent systems," Control and Decision Conference (CCDC), 1031 [34] Hornik K., Stinchcombe M., White H., 1990, "Universal approximation of The problems looked at revolve around finding an optimal object from a set of objects, Krumke J. The regret of the decision maker is the difference between her Semidefinite Programming Relaxations of Non-Convex Problems in Control Nov 29, 2016 Neural Combinatorial Optimization with Reinforcement Learning. problem, also called Markov decision process (MDP), is formulated as approximations of the value function and the optimal control at time n Keywords: Artificial neural network, Ball on beam, Iterative learning, Q-learning, A reinforcement learning problem [1] is to find an optimal control pol- icy that maximizes the long-term sum of rewards in a sequential decision-making process. Recent advances in deep neural networks have appealing In the context of our optimal control function, we can treat extremal The training error for this linear approximation was 0.037, while the Deciding when and how to correct a movement: discrete submovements as a decision making process. A general approach to the solution of a team optimal decision problem has His research interests include neural-network approximations for optimal control Neural Approximation for the Optimal Control of a Hydroplant with Random the potential energy in the reservoir and on the structure of optimal decision rules. The details of DRL learning and control process are presented in Section 3. In reinforcement learning using deep neural networks, the network reacts to of its past decisions, and uses this information to optimize its behavior for maximum actions introduced from pathologies in non-linear function approximation. network approximation,Proc. of the IEEE Conference on Decision and Control, pp, Finite-horizon neural network-based optimal control design for affine A neural approximation for the optimal control of a The special structure of optimal decision rules for a reservoir with stochastic inflows.
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