Global ETD Search

Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination

Wiryana, I. M.Edith Cowan University, Research Online, Perth, Western Australia1994

… fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set …

edithcowan Repository record for Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination (opens in a new tab)

Private and Provably Efficient Federated Decision-Making

Dubey, AbhimanyuMassachusetts Institute of Technology2022

… both the multi-agent and federated setting with linear function approximation. We propose variants of least-squares value iteration algorithms that are provably no-regret with only a constant communication budget. We believe that the future of machine learning entails large-scale cooperation …

mit Repository record for Private and Provably Efficient Federated Decision-Making (opens in a new tab)

Learning Probabilistic Generative Models For Fast Sampling-Based Planning

Huh, Jinwook2019

… we suggest a sampling method with a learned Q-function with linear function approximation based on feature representations such as Radial Basis Functions. This sampling method chooses the optimal node from which to extend the search tree via the softmax function of learned state values. We also …

penn Repository record for Learning Probabilistic Generative Models For Fast Sampling-Based Planning (opens in a new tab)

Reinforcement learning for multi-agent and robust control systems

Zhang, KaiqingUniversity of Illinois at Urbana-Champaign2022

… and establish their convergence guarantees when linear function approximation is used. Setting ii corresponds to a classical robust control problem, with linear dynamics and robustness concerns in the H∞-norm sense. In contrast to existing solvers, we introduce policy-gradient methods to solve …

uiuc Repository record for Reinforcement learning for multi-agent and robust control systems (opens in a new tab)

A Comparison of the Performance of Neural Q-learning and Soar-RL on a Derivative of the Block Design (BD)/Block Design Multiple Choice (BDMC) Subtests on the WISC-IV Intelligence Test

Bell, Charreau

clemson Repository record for A Comparison of the Performance of Neural Q-learning and Soar-RL on a Derivative of the Block Design (BD)/Block Design Multiple Choice (BDMC) Subtests on the WISC-IV Intelligence Test (opens in a new tab)

Multiagent planning and learning using random decompositions and adaptive representations

Üre, Nazim KemalMassachusetts Institute of Technology2015

… extensive parameter tuning. Each agent learns a linear function approximation of the actual model, and the number of features is increased incrementally to automatically adjust the model complexity based on the observed data. These features are compact representations of the key characteristics …

mit Repository record for Multiagent planning and learning using random decompositions and adaptive representations (opens in a new tab)