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Showing 1 to 9 of 9 for “"Value function approximation"”.
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Sparse Value Function Approximation for Reinforcement Learning
… reinforcement learning (RL) algorithms is the approximation of the value function. The design and selection of features for approximation in RL is crucial, and an ongoing area of research. One approach to the problem of feature selection is to apply sparsity-inducing techniques in learning the …
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Value function approximation architectures for neuro-dynamic programming
… only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen? In this dissertation, we first propose an approach using the solutions to associated fluid and diffusion approximations. In order to evaluate this …
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Learning and value function approximation in complex decision processes
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
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Multiple machine maintenance : applying a separable value function approximation to a variation of the multiarmed bandit
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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Electric Vehicle Fleet Charging Management
… to replace the charging decisions' expected value. It employs a value function approximation, enabling rapid charging solutions, even for large fleets, with competitive costs and service levels. Lastly, the dissertation examines a decision support system (DSS) integrating optimal stopping …
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Dynamic Programming Decomposition Methods For Capacity Allocation And Network Revenue Management Problems
… dynamic program. We show that the proposed approximations have two important characteristics: First, they provide relatively tight performance bounds on the optimal value of the stochastic optimization problem under consideration. Second, they give rise to policies that on average perform …
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Approximate dynamic programming for large scale systems
… can be cast as dynamic programs and the optimal value function can be computed by solving Bellman's equation. However, this approach is limited in its applicability. As the number of state variables increases, the state space size grows exponentially, a phenomenon known as the curse of …
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Active Suppression ofAerofoil Flutter via Neural-Network-Based Adaptive Nonlinear Optimal Control
… follow. Secondly, a Modified form of NN-based Value Function Approximation (MVFA), tuned by gradient-descent learning, is proposed for NOCOS to address the closedloop stability in a compact controller configuration suitable for real-time implementation. Its validity and efficacy are examined by …